A survey of advances in vision-based vehicle re-identification
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paper_title: Urban Computing: Concepts, Methodologies, and Applications

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Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.
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paper_title: Reliable Classification of Vehicle Types Based on Cascade Classifier Ensembles

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Vehicle-type recognition based on images is a challenging task. This paper comparatively studied two feature extraction methods for image description, i.e., the Gabor wavelet transform and the Pyramid Histogram of Oriented Gradients (PHOG). The Gabor transform has been widely adopted to extract image features for various vision tasks. PHOG has the superiority in its description of more discriminating information. A highly reliable classification scheme was proposed by cascade classifier ensembles with reject option to accommodate the situations where no decision should be made if there exists adequate ambiguity. The first ensemble is heterogeneous, consisting of several classifiers, including k-nearest neighbors (kNNs), multiple-layer perceptrons (MLPs), support vector machines (SVMs), and random forest. The classification reliability is further enhanced by a second classifier ensemble, which is composed of a set of base MLPs coordinated by an ensemble metalearning method called rotation forest (RF). For both of the ensembles, rejection option is accomplished by relating the consensus degree from majority voting to a confidence measure and by abstaining to classify ambiguous samples if the consensus degree is lower than a threshold. The final class label is assigned by dual majority voting from the two ensembles. Experimental results using more than 600 images from a variety of 21 makes of cars and vans demonstrated the effectiveness of the proposed approach. The cascade ensembles produce consistently reliable results. With a moderate ensemble size of 25 in the second ensemble, the two-stage classification scheme offers 98.65% accuracy with a rejection rate of 2.5%, exhibiting promising potential for real-world applications.
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paper_title: Online Multiple Maneuvering Vehicle Tracking System Based on Multi-Model Smooth Variable Structure Filter

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Autonomous vehicles need a real-time traffic tracking system in order to interact with multiple moving vehicles in urban situations. Most existing works rely on camera sensors to interpret the surrounding environment. However, camera sensors degrade in conditions of lighting, shadows, and extreme weathers such that they can hardly detect objects. The light detection and ranging (LiDAR) sensors promise to be a good substitute, as they enable highly precise and robust localization across a wide range of conditions. This paper presents a new LiDAR-based online multiple maneuvering vehicle tracking problem and proposes a novel online multi-model smooth variable structure filter to address the problem. The real-time experiments show that our method is able to deliver superior performance compared to other conventional methods.
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paper_title: DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

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Vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for urban traffic surveillance. In this paper, we present a fast framework of Detection and Annotation for Vehicles (DAVE), which effectively combines vehicle detection and attributes annotation. DAVE consists of two convolutional neural networks (CNNs): a fast vehicle proposal network (FVPN) for vehicle-like objects extraction and an attributes learning network (ALN) aiming to verify each proposal and infer each vehicle’s pose, color and type simultaneously. These two nets are jointly optimized so that abundant latent knowledge learned from the ALN can be exploited to guide FVPN training. Once the system is trained, it can achieve efficient vehicle detection and annotation for real-world traffic surveillance data. We evaluate DAVE on a new self-collected UTS dataset and the public PASCAL VOC2007 car and LISA 2010 datasets, with consistent improvements over existing algorithms.
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paper_title: On-road vehicle detection: a review

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Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.
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paper_title: Traffic accident detection through a hydrodynamic lens

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In this paper we present a novel method for automatic traffic accident detection, based on Smoothed Particles Hydrodynamics (SPH). In our method, a motion flow field is obtained from the video through dense optical flow extraction. Then a thermal diffusion process (TDP) is exploited to turn the motion flow field into a coherent motion field. Approximating the moving particles to individuals, their interaction forces, represented as endothermic reactions, are computed using the enthalpy measure, thus obtaining the potential particles of interest. Furthermore, we exploit SPH that accumulates the contribution of each particle in a weighted form, based on a kernel function. The experimental evaluation is conducted on a set of video sequences collected from Youtube, and the obtained results are compared against a state of the art technique.
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paper_title: Self-Control of Traffic Lights and Vehicle Flows in Urban Road Networks

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Based on fluid-dynamic and many-particle (car-following) simulations of traffic flows in (urban) networks, we study the problem of coordinating incompatible traffic flows at intersections. Inspired by the observation of self-organized oscillations of pedestrian flows at bottlenecks, we propose a self-organization approach to traffic light control. The problem can be treated as a multi-agent problem with interactions between vehicles and traffic lights. Specifically, our approach assumes a priority-based control of traffic lights by the vehicle flows themselves, taking into account short-sighted anticipation of vehicle flows and platoons. The considered local interactions lead to emergent coordination patterns such as 'green waves' and achieve an efficient, decentralized traffic light control. While the proposed self-control adapts flexibly to local flow conditions and often leads to non-cyclical switching patterns with changing service sequences of different traffic flows, an almost periodic service may evolve under certain conditions and suggests the existence of a spontaneous synchronization of traffic lights despite the varying delays due to variable vehicle queues and travel times. The self-organized traffic light control is based on an optimization and a stabilization rule, each of which performs poorly at high utilizations of the road network, while their proper combination reaches a superior performance. The result is a considerable reduction not only in the average travel times, but also of their variation. Similar control approaches could be applied to the coordination of logistic and production processes.
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paper_title: Structured learning for crowd motion segmentation

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In this paper we present a novel method for motion segmentation in crowded scenes, based on statistical modeling for structured prediction using a Conditional Random Field (CRF). As opposed to other conditional Markov models, CRF overcomes the label bias problem, making it suitable for crowd motion analysis. In our method, a grid of particles is initialized on the scene, and advected using optical flow. The particles are exploited to extract motion patterns, used as input priors for CRF training. Furthermore, we exploit min cut/max flow algorithm to remove the residual noise and highlight the main directions of crowd motion. The experimental evaluation is conducted on a set of benchmark video sequences, commonly used for crowd motion analysis, and the obtained results are compared against other state of the art techniques.
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paper_title: Adaptive video-based algorithm for accident detection on highways

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For the past few decades, automatic accident detection, especially using video analysis, has become a very important subject. It is important not only for traffic management but also, for Intelligent Transportation Systems (ITS) through its contribution to avoid the escalation of accidents especially on highways. In this paper a novel vision-based road accident detection algorithm on highways and expressways is proposed. This algorithm is based on an adaptive traffic motion flow modeling technique, using Farneback Optical Flow for motions detection and a statistic heuristic method for accident detection. The algorithm was applied on a set of collected videos of traffic and accidents on highways. The results prove the efficiency and practicability of the proposed algorithm using only 240 frames for traffic motion modeling. This method avoids to utilization of a large database while adequate and common accidents videos benchmarks do not exist.
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paper_title: Facing Needs and Requirements of Crowd Modelling: Towards a Dedicated Computer Vision Toolset

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The modelling and simulation of pedestrians and crowd dynamics require empirical evidences and quantitative data describing the relevant phenomena that models must be able to reproduce. Computer vision can provide several tools both to semi-automatically acquire the demand of a given situation and actually configure a simulation model, as well as to gather information for the sake of model calibration and validation. This paper proposes methods supporting the segmentation and pedestrian counting of crowd flows, the identification and characterisation of main flows in an analysed scene and the detection of social groups in an observed population. The methods are briefly introduced and the achieved results are presented and discussed with reference to the current state of the art.
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paper_title: Vehicle Color Recognition on Urban Road by Feature Context

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Vehicle information recognition is a key component of intelligent transportation systems. Color plays an important role in vehicle identification. As a vehicle has its inner structure, the main challenge of vehicle color recognition is to select the region of interest (ROI) for recognizing its dominant color. In this paper, we propose a method to implicitly select the ROI for color recognition. Preprocessing is performed to overcome the influence of image quality degradation. Then, the ROI in vehicle images is selected by assigning the subregions with different weights that are learned by a classifier trained on the vehicle images. We train the classifier by linear support vector machine for its efficiency and high precision. The experiments are extensively validated on both images and videos, which are collected on urban roads. The proposed method outperforms other competing color recognition methods.
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paper_title: Cross-camera vehicle tracking via affine invariant object matching for video forensics applications

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The recent deployment of very large-scale camera networks consisting of fixed/moving surveillance cameras and vehicle video recorders, has led to a novel field in object tracking problem. The major goal is to detect and track each vehicle within a large area, which can be applied to video forensics. For example, a suspected vehicle can be automatically identified for mining digital criminal evidences from a large amount of video data. In this paper, we propose an efficient cross-camera vehicle tracking technique via affine invariant object matching. More specifically, we formulate the problem as invariant image feature matching among different viewpoints of cameras. To achieve vehicle matching, we first extract invariant image feature based on ASIFT (affine and scale-invariant feature transform) for each detected vehicle in a camera network. Then, to improve the accuracy of ASIFT feature matching between images from different viewpoints, we propose to efficiently match feature points based on our observed spatially invariant property of ASIFT, as well as the min-hash technique. As a result, cross-camera vehicle tracking can be efficiently and accurately achieved. Experimental results demonstrate the efficacy of the proposed algorithm and the feasibility to video forensics applications.
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paper_title: Density independent hydrodynamics model for crowd coherency detection

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We propose density independent hydrodynamics model (DIHM) which is a novel and automatic method for coherency detection in crowded scenes. One of the major advantages of the DIHM is its capability to handle changing density over time. Moreover, the DIHM avoids oversegmentation and thus achieves refined coherency detection. In the proposed DIHM, we first extract a motion flow field from the input video through particle initialization and dense optical flow. The particles of interest are then collected to retain only the most motile and informative particles. To represent each particle, we accumulate the contribution of each particle in a weighted form, based on a kernel function. Next, the smoothed particle hydrodynamics (SPH) is adopted to detect coherent regions. Finally, the detected coherent regions are refined to remove the effects of oversegmentation. We perform extensive experiments on three benchmark datasets and compare the results with 10 state-of-the-art coherency detection methods. Our results show that DIHM achieves superior coherency detection and outperforms the compared methods in both pixel level and coherent region level average particle error rates (PERs), average coherent number error (CNE) and F-score.
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paper_title: HoG based real-time multi-target tracking in Bayesian framework

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Multi-target tracking is one of the most challenging tasks in computer vision. Several complex techniques have been proposed in the literature to tackle the problem. The main idea of such approaches is to find an optimal set of trajectories within a temporal window. The performance of such approaches are fairly good but their computational complexity is too high making them unpractical. In this paper, we propose a novel tracking-by-detection approach in a Bayesian filtering framework. The appearance of a target is modeled through HoG descriptor and the critical problem of target association is solved through combinatorial optimization. It is a simple yet very efficient approach and experimental results show that it achieves state-of-the-art performance in real time.
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paper_title: Appearance-based methods in re-identification: A brief review

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In this paper a brief review of appearance-based methods of re-identification in surveillance systems is presented. Previous work involving various parts of re-identification task is discussed and described. The paper concludes in term of re-identification challenges and future work direction.
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paper_title: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with ’attention’ mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3] , our detection system has a frame rate of 5 fps ( including all steps ) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.
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paper_title: Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control

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Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.
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paper_title: Building a comprehensive ontology to refine video concept detection

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Recent research has discovered that leveraging ontology is an effective way to facilitate semantic video concept detection. As an explicit knowledge representation, a formal ontology definition usually consists of a lexicon, properties, and relations. In this paper, we present a comprehensive representation scheme for video semantic ontology in which all the three components are well studied. Specifically, we leverage LSCOM to construct the concept lexicon, describe concept property as the weights of different modalities which are obtained manually or by data-driven approach, and model two types of concept relations (i.e., pairwise concept correlation and hierarchical relation). In contrast with most existing ontologies which are only focused on one or two components for domain-specific videos, the proposed ontology is more comprehensive and general. To validate the effectiveness of this ontology, we further apply it to video concept detection. The experiments on TRECVID 2005 corpus have demonstrated a superior performance compared to existing key approaches to video concept detection.
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paper_title: Freeway Control Systems for Using Section-Related Traffic Variable Detection

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Traffic control systems for freeways are described which use section-related traffic variable measurement techniques. For deriving section-related variables feature sequence of a vehicle series of one measurement site is correlated with the feature sequence of the adjacent measurement site. As features signature characteristics of the inductive loop detuning curve are used. The evaluation unit for feature extraction is implemented on a VME-bus master CPU. Practical experiences which are reported from a traffic control system on the freeway B14 between Fellbach and Stuttgart show a more reliable and quicker automatic incident detection in comparison with local measurement techniques.
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paper_title: Evaluation of Overhead and In-Ground Vehicle Detector Technologies for Traffic Flow Measurement

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As part of the U.S. FHWA-sponsored Detection Technology for IVHS program, ultrasonic, microwave radar, infrared laser radar, nonimaging passive infrared, video image processing with visible and infrared spectrum imagery, acoustic array, high sampling rate inductive loop, conventional inductive loop, microloop, and magnetometer detector technologies were evaluated at freeway and surface street arterial sites in Minnesota, Florida, and Arizona. These states were chosen because they exhibited a wide range of climatic conditions. The criteria for selecting the detector evaluation sites included searching for roadways with high traffic density and suitable structures for mounting the overhead detectors. Approximately 5.9 Gbytes of digital and analog vehicle detection and signature data and more than 300 video tapes of the corresponding traffic flow were recorded. The detector outputs were time tagged and recorded on 88 Mbyte magnetic cartridges by using a data logger specifically designed and built for this project. Detectors with serial RS-232 outputs required interface software to be written for each unique data structure. Data analysis software was also written to convert the raw data into an easily accessible Paradox database format compatible with a Windows personal computer operating system. Traffic volume ground truth data, obtained by counting vehicles from the recorded video imagery, were compared with the counts from the detector outputs. Speed ground truth data, obtained by driving probe vehicles through the field of view of the detectors and noting their speed as measured by the vehicle's speedometer, were compared with the speed measurement from the detectors. Several types of detectors were found to satisfy current traffic management requirements. However, improved accuracies and new types of information, such as queue length and vehicle turning or erratic movements, may be required from detectors for future traffic management applications.
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paper_title: Vehicle re-identification with a single magnetic sensor

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Vehicle re-identification enables the calculation of travel times and origin-destination matrices. These are key data for modeling and therefore optimizing the traffic flow. This paper compares the performances of different vehicle re-identification methods using the vehicle tridimensional magnetic signature recorded with a single three-axis magnetic sensor. These methods based on temporal distances and on features extraction are evaluated. The results obtained on a large data-base composed of signatures from 25 different vehicles show that the tridimensional Dynamic Time Warping is able to correctly re-identify 80% of the vehicles without any false alarms. To reduce its computational time, it can be combined with a Gaussian maximum likelihood classifier using temporal features through cascading rules without significantly reducing the re-identification performances. Moreover, the influence of the vehicle orientation to the North on the re-identification performances is evaluated and shown to be limited, which enables the re-identification of vehicles after a change of direction.
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paper_title: Improving similarity measures for re-identification of vehicles using AMR sensors

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The use of Anisotropic Magneto-Resistive (AMR) sensors for road traffic monitoring has gained popularity in recent years. The Earth magnetic field variations caused by vehicles passing over this kind of sensors can be measured and used for various purposes, including vehicle re-identification at different sensing zones. AMR sensors provide data that allow the calculation of traffic parameters by using simple signal processing techniques. But when vehicle re-identification is the objective, it is not clear which is the best method to compare the signals from different sensors in order to know whether or not they belong to the same vehicle. This paper presents an experimental study with the objective of determining the optimal similarity measure function for AMR sensors signals when used for vehicle re-identification.
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paper_title: STRUCTURAL RANGE IMAGE TARGET MATCHING FOR AUTOMATED LINK TRAVEL TIME COMPUTATION

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Measurement of travel time along a roadway link between two known points has historically been a task which consumed much time and manpower. Currently, probe vehicles are driven along the link a number of times to achieve an average time measurement. This type of approach, because of the cost and effort involved, can only be repeated on an inadequate periodic basis. The advances in technology being applied to Intelligent Transport Systems will allow for automated Link Travel Time computation. This paper will present a method to measure Link Travel Time by matching approximately 10% of the vehicles detected by laser-rangefinder-based sensors, such as the Autosense family of sensors developed by Schwartz Electro-Optics, at each end of the link. An overview of the Autosense sensor will be included to illustrate the type of data being utilized by the system. The main components of the algorithm to be presented include primitive extraction from the range data, structural matching to pair off the extracted vehicle primitives from the two sensor locations and sample set selection. The approach utilizes a novel concept of computing the match scores for every combination of vehicle pairs before choosing the sample set to be used for travel time measurement. The pruning of the complete set of matched vehicle pairs to the most confidently matched pairs insures a more accurate time assessment. Preliminary results show that, of the vehicles chosen by the system as sample vehicles, 99.7% are correctly matched. Results from tests run at two sensor sites being built on State Rd 441 in the summer of 1996 will be presented.
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paper_title: Vehicle re-identification using wireless magnetic sensors: Algorithm revision, modifications and performance analysis

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A vehicle re-identification method based on matching vehicle signatures obtained from wireless magnetic sensors was studied on a single lane loop on-ramp. Different modifications were implemented in the algorithm in order to address limitations of the system when vehicles stop/move slowly over the detectors. The original and modified vehicle re-identification algorithm results were compared against ground truth data obtained from video. Based on the ground truth data it was possible to determine the percentage of vehicles that are re-identified and the number of those vehicles that are misidentified as a function of different algorithm parameters. For this analysis, vehicles were divided into two subsets: i) uncongested and ii) congested. The original method mismatched percentage was around or below 15% for the uncongested vehicle subset and between 20% to 60% for the congested one. With the modified method it was possible to improve the matching rate as well as the accuracy of the matching algorithm. For the uncongested subset, the modified method showed a higher vehicle re-identification rate while maintaining the mismatched percentage around or below 8%. The main improvement over the original method was achieved on the congested vehicle subset, since the number of re-identified vehicles was increased over the original method while keeping the mismatched percentage around or below 14%.
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paper_title: PROBING FOR TRAVEL TIME : NORWAY APPLIES AVI AND WIM TECHNOLOGIES FOR SECTION PROBE DATA

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This article reviews work to date in Norway on automatic vehicle identification (AVI) toll tag- and weigh-in-motion sensor based systems.
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paper_title: VEHICLE DETECTION AND COMPASS APPLICATIONS USING AMR MAGNETIC SENSORS

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This paper presents a review of magnetic sensing and discusses applications for magnetic sensors. Focus is on magnetic sensing in vehicle detection and navigation that is based on magnetic fields.
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paper_title: Travel-time estimation using cross-correlation techniques

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This paper demonstrates the viability of using cross-correlation techniques with inductance loop data to measure the propagation time of traffic. The propagation time between loops separated by 0.5 miles is measured by using time-average activation counts ("volume") from inductance loops. The resulting time delay is used to estimate the mean speed. This independent speed estimate can be used to improve the volume/occupancy ratio estimate of speed from single loops.
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paper_title: Multiple inductive loop detectors for intelligent transportation systems applications: Ramp metering, vehicle re-identification and lane change monitoring systems

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Implementation of Intelligent Transportation System (ITS) applications such as ramp metering, vehicle reidentification and lane change monitoring systems in a heterogeneous and lane less traffic is a challenging task. The present study proposes possible solutions to implement such applications using a newly developed multiple inductive loop detector system. Prototypes of these applications have been developed and tested in the field.
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paper_title: Freeway Control Systems for Using Section-Related Traffic Variable Detection

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Traffic control systems for freeways are described which use section-related traffic variable measurement techniques. For deriving section-related variables feature sequence of a vehicle series of one measurement site is correlated with the feature sequence of the adjacent measurement site. As features signature characteristics of the inductive loop detuning curve are used. The evaluation unit for feature extraction is implemented on a VME-bus master CPU. Practical experiences which are reported from a traffic control system on the freeway B14 between Fellbach and Stuttgart show a more reliable and quicker automatic incident detection in comparison with local measurement techniques.
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paper_title: Re-identification by Inductive Loop Detector: Experimentation on target origin — Destination matrix

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Traffic congestion has led to research on how to use the existing infrastructure more efficiently. The experimental platform SAROT was constructed to allow traffic analysis but also to test new algorithms for traffic modeling and management. Origin-Destination (OD) Matrix is one of the most important traffic information for Intelligent Traffic System. By using the experimental platform SAROT, we want to test algorithms for OD Matrix. To obtain OD matrix, we need to get useful and accurate data by tracking vehicles. Two approaches for tracking vehicles by using Inductive Loop Detector (ILD) are used. Both approaches are computed on real traffic data set and compared. We use threshold decision to improve the correct matching rate for tracking vehicles. We propose a new methodology by associating two algorithms for increasing the correct matching rate. The new methodology was used to perform target OD matrix.
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paper_title: Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways

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Abstract The vehicle reidentification problem is the task of matching a vehicle detected at one location with the same vehicle detected at another location from a feasible set of candidate vehicles detected at the other location. This paper formulates and solves the vehicle reidentification problem as a lexicographic optimization problem. Lexicographic optimization is a preemptive multi-objective formulation, and this lexicographic optimization formulation combines lexicographic goal programming, classification, and Bayesian analysis techniques. The solution of the vehicle reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands. Implementation of this approach using conventional surveillance infrastructure permits the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems). Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions.
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paper_title: Day-to-Day Travel Time Trends and Travel Time Prediction from Loop Detector Data

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An approach is presented for estimating future travel times on a freeway using flow and occupancy data from single-loop detectors and historical travel-time information. Linear regression, with the stepwise-variable-selection method and more advanced tree-based methods, is used. The analysis considers forecasts ranging from a few minutes into the future up to an hour ahead. Leave-a-day-out cross-validation was used to evaluate the prediction errors without underestimation. The current traffic state proved to be a good predictor for the near future, up to 20 min, whereas historical data are more informative for longer-range predictions. Tree-based methods and linear regression both performed satisfactorily, showing slightly different qualitative behaviors for each condition examined in this analysis. Unlike preceding works that rely on simulation, real traffic data were used. Although the current implementation uses measured travel times from probe vehicles, the ultimate goal is an autonomous system that r...
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paper_title: A signal processing framework for vehicle re-identification and travel time estimation

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Link travel times are crucial information for advanced traveler information system and traffic management applications. However, current systems for estimating them have many limitations. In order to improve travel time estimation, a novel framework for vehicle re-identification via signature matching is proposed. Individual vehicles are matched between well-separated stations in a road transportation network using signatures captured by roadway embedded sensors. Statistical signal processing techniques are used to develop robust signature pre-processing algorithms that support the subsequent signature matching problem, which is formulated using techniques from communication theory. A probabilistic modeling of the observed matching assignments is used to devise a travel time estimation algorithm that is robust to potential misidentifications. The proposed method is tested under real traffic scenarios and satisfactory link travel time measures are reported.
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paper_title: Real Time Vehicle Reidentification and Performance Measures on Signalized Arterials

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An analytical model was developed and tested to estimate the travel times on signalized arterials in real-time based on data commonly provided by system loop detectors (flow and occupancy) and the signal settings (cycle length, green times and offsets). The model was extended to account for the effects of detector placement and queue spillovers. The results from model applications show that it accurately estimates travel times for a wide range of operating conditions. A vehicle re-identification methodology also was developed based on data from conventional loop detectors. Sample application indicates that it can be used to produce performance measures such as travel time variability and quality of signal progression
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paper_title: Vehicle Detector Signature Processing and Vehicle Reidentification for Travel Time Estimation

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Accurate travel time estimation has been shown to be crucial information for traffic flow management systems. In this paper, we present the foundations for a travel time estimation methodology using vehicle signatures with special attention to signature pre-processing. This approach to signature pre-processing is based on principled multi-rate signal processing techniques. The matching of signatures from an upstream station to a downstream station is done through a maximum-a-posteriori (MAP) classifier. For each matched upstream-to-downstream pair of signatures, the feasibility of the corresponding travel time is used to filter out mismatches. Travel time is obtained by computing the median of the observations that fall within the modal bin of the travel time histogram. Best results are obtained when only the subset of vehicles with atypical signatures is considered. Although, any types of inductive or magneto-inductive signatures can be appropriately in the presented method, the best performances are expected when the sensors in use generate signatures with a higher degree of pair-wise dissimilarity. Both 3M micro-loops and the standard ILDs (Inductive Loop Detector) were used in this study. In the experimental tests (shorter station separation), the proposed signature pre-processing procedure, when applied to all types of vehicles in the traffic stream and without feasibility constraints, lead to 67.3% re-identification accuracy. When feasibility constraints are still absent and only atypical signatures were considered, the re-identification performance reached 97.4%. These results are very promising since in the absence of feasibility constraints, the performance the re-identification algorithm is independent of the separation between the stations.
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paper_title: Vehicle Reidentification with the Inductive Loop Signature Technology

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This study proposed a real-time inductive loop signature based vehicle reidentification approach, RTREID-2M, which improved the previously developed RTREID-2 algorithm on two aspects: (1) Develop a cubic spline data imputation approach to replace the existing linear data imputation approach in order to improve the raw signature data quality; (2) Improve the time window setting for vehicles on High-Occupancy Vehicle (HOV) lanes so that vehicles on HOV lanes traveling with free flow speeds would be considered when generating candidate vehicle sets even during congestion time periods. In addition, a stratified-random sampling method was developed to effectively perform ground-truthing task for evaluating the performance of the proposed RTREID-2M. The evaluation results showed desired performance for vehicle reidentification and travel time estimation under both free-flow and congested flow traffic conditions. The future research will focus on the potential applications and arterial vehicle reidentification utilizing the inductive loop signature technologies.
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paper_title: Real-time freeway level of service using inductive-signature-based vehicle reidentification system

paper_content:

The Highway Capacity Manual provides a method for determining the level of service (LOS) on freeways to evaluate freeway performance. Apart from being essentially an off-line decision support tool for planning and design, it is also based on point measurements from loop detectors, which may not provide an accurate assessment of freeway section performance. In order to meet user requirements of advanced traffic management and information systems, new LOS criteria based on section measures are required for real-time freeway analysis. The main aim of this research was to demonstrate a technique for development of such LOS criteria. The study uses a new measure of effectiveness, called reidentified median section speed (RMSS), derived from analysis of inductive vehicle signatures and reidentification of vehicles traveling through a major section of freeway in the City of Irvine, CA. Two main issues regarding real-time LOS criteria were addressed. The first was how to determine the threshold values partitioning the LOS categories. To provide reliable real-time traffic information, the threshold values should be decided such that RMSSs within the same LOS category represent similar traffic conditions as much as possible. In addition, RMSSs in different LOS categories should represent dissimilar traffic conditions. The second issue concerned the aggregation interval to use for deriving LOS categories. Two clustering techniques were then employed to derive LOS categories, namely, k-means and fuzzy approaches. Wilk's Lambda analysis and LOS stability analysis were performed to design new LOS criteria. Six LOS categories defined in terms of RMSS over a fixed 240-s interval were identified as the best solution to meet two major considerations described above. The procedures used in this study are readily transferable to other similarly equipped freeway sections for the derivation of real-time LOS.
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paper_title: Accurate Vehicle Location System Using RFID, an Internet of Things Approach

paper_content:

Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved.
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paper_title: Travel Time Estimation Using Cell Phones (TTECP) for Highways and Roadways

paper_content:

Across the USA, there has been interest among the State Departments of Transportation (DOTs) to investigate alternative technologies to estimate travel time such as the use of cell phones as travel probes. This project "Travel Time Estimation using Cell Phones (TTECP) for Highways and Roadways" was initiated by the Florida Department of Transportation (FDOT) at Florida International University (FIU) in September 2004. This report summarizes our studies and investigations regarding the maturity of cell phone technologies for application as real-time traffic probes for travel time estimations along the highways and roadways. Also, the reliability, accuracy, and reproducibility of the travel speed and travel time computations based upon the cell phones as travel probes have been investigated and compared with other methods such as the 511-based operations and Global Positioning System (GPS) - based measurements. The FIU TTECP team has found by virtue of their investigations that the cell phone technology is viable and mature under the normal conditions of free traffic flow, for the travel time estimations. However, the team has also found that the cell phone technology is not accurate in congested traffic conditions, where the data is more important than in the free-flow traffic conditions, and the accuracy decreases rapidly as the congestion increases. The details are provided in the executive summary and the conclusion sections. Additional issues remain such as: (1) privacy of the cell phone users whose phone transmissions are being probed by the cell companies for location data, (2) irregular and transient cell data for travel time and speed computations, especially during congested traffic and severe weather conditions, (3) limited capabilities of the travel time providers to follow changes by the cell companies in their data formats and structures, and (4) incompatibility of data when switching from one travel time provider to another (with different affiliations with the cell phone companies). These issues should be addressed and viable solutions should be found.
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paper_title: TRAVEL TIME DATA COLLECTION HANDBOOK

paper_content:

This Travel Time Data Collection Handbook provides guidance to transportation professionals and practitioners for the collection, reduction, and presentation of travel time data. The handbook should be a useful reference for designing travel time data collection efforts and systems, performing travel time studies, and reducing and presenting travel time data. Agencies new to travel time data collection may wish to adopt sections of the handbook as standard procedures, wehreas agencies with data collection experience may wish to incorporate specific details or criteria. Initial chapters of the handbook describe how to design data collection activities, including the determination of parameters such as study size and scope, data collection technique, and other critical study elements. Chapters 3 through 6 include a description of each data collection technique, major advantages and disadvantages, cost and equipment requirements, and step-by-step instructions. Related experience with the data collection techniques is included for examples of applications. Chapter 7 provides guidance for reducing travel time data and preparing tabular and graphical presentations. The chapters of the handbook are as follows: Chapter 1, Introduction; Chapter 2, Developing and Implementing a Data Collection Plan; Chapter 3, Test Vehicle Techniques; Chapter 4, License Plate Matching Techniques; Chapter 5, ITS Probe Vehicle Techniques; Chapter 6, Emerging and Non-Traditional Techniques; and Chapter 7, Data Reduction, Summary, and Presentation.
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paper_title: Using GPS Data to Gain Insight into Public Transport Travel Time Variability

paper_content:

Transit service reliability is an important determinant of service quality, which has been mainly studied from the perspective of passengers waiting at stops. Day-to-day variability of travel time also deteriorates service reliability, but is not a well-researched area in the literature partly due to the lack of comprehensive data sets on bus travel times. While this problem is now being addressed through the uptake of global positioning system (GPS)-based tracking systems, methodologies to analyze these data sets are limited. This paper addresses this issue by investigating day-to-day variability in public transport travel time using a GPS data set for a bus route in Melbourne, Australia. It explores the nature and shape of travel time distributions for different departure time windows at different times of the day. Factors causing travel time variability of public transport are also explored using a linear regression analysis. The results show that in narrower departure time windows, travel time distributions are best characterized by normal distributions. For wider departure time windows, peak-hour travel times follow normal distributions, while off-peak travel times follow lognormal distributions. The factors contributing to the variability of travel times are found to be land use, route length, number of traffic signals, number of bus stops, and departure delay relative to the scheduled departure time. Travel time variability is higher in the AM peak and lower in the off-peak. The impact of rainfall on travel time variability is only found significant in the AM peak. While the paper presents new methods for analyzing GPS-based data, there is much scope for expanding knowledge through wider applications to new data sets and using a wider range of explanatory variables.
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paper_title: CELLPHONE PROBES AS AN ATMS TOOL

paper_content:

The foundation of traffic operations and management is the ability to monitor traffic conditions. One approach to traffic monitoring is to sample conditions by tracking a limited number of probe vehicles as they traverse a network. An emerging technology known as wireless location technology (WLT) has been developed to allow for the geolocation of mobile wireless devices (the most common of which are cellular telephones). Over the past decade, a number of research studies and operational tests have attempted to develop probe traffic monitoring systems based on WLT. However, there still exists significant confusion and misperceptions concerning WLT-based traffic monitoring. To address this problem, this paper seeks to provide a comprehensive assessment of WLT-based traffic monitoring. To do so the specific purposes of this paper are to: (a) fully describe the concept of WLT-based traffic monitoring, (b) present a critical assessment of past studies of WLT-based traffic monitoring, (c) document the evaluation of one of the most recent operational tests the 2001 Virginia Department of Transportation (VDOT), Maryland State Highway Administration (MSHA), and US Wireless Corporation (USWC) effort in the Washington, D.C. region, and (d) discuss the unique challenges that these systems pose to the field of traffic engineering
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paper_title: Dynamic origin-destination demand estimation using automatic vehicle identification data

paper_content:

This paper proposes a dynamic origin-destination (OD) estimation method to extract valuable point-to-point split-fraction information from automatic vehicle identification (AVI) counts without estimating market-penetration rates and identification rates of AVI tags. A nonlinear ordinary least-squares estimation model is presented to combine AVI counts, link counts, and historical demand information into a multiobjective optimization framework. A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors, and the resulting optimization problems are solved by using an iterative bilevel estimation procedure. Based on a synthetic data set, this study shows the effectiveness of the proposed estimation models under different market-penetration rates and identification rates
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paper_title: An improved technique to discover compromising electromagnetic emanations

paper_content:

The techniques generally used to detect compromising emanations are based on a wide-band receiver tuned on a specific frequency or a spectral analyzer with a limited bandwidth. However, these methods may not be optimal since a significant amount of information is lost during the signal acquisition. In this paper, we propose a straightforward but efficient approach which acquires raw signal directly from the antenna and processes the entire captured electromagnetic spectrum thanks to the computation of short time Fourier transforms. We applied this approach to detect potential compromising electromagnetic emanations radiated by modern keyboard. Since keyboards are often used to transmit confidential data such as passwords, these emanations could remotely reveal sensitive information such as keystrokes. Thanks to this method, we detected four different kinds of compromising electromagnetic emanations generated by wired and wireless keyboards. These emissions lead to a full or a partial recovery of the keystrokes. We implemented these side-channel attacks and our best practical attack fully recovered 95% of the keystrokes of a PS/2 keyboard at a distance up to 20 meters, even through walls.
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paper_title: Link travel time estimation in urban areas by detectors and probe vehicles fusion

paper_content:

This paper presents an approach to estimate link travel time in urban areas. This approach consists of a data fusion from underground loop detectors and probe vehicles equipped with global positioning system (GPS). This method is expected to be more accurate, reliable and robust than using either of these data sources alone. In this approach, an algorithm is developed. This algorithm is based on the unscented Kalman filter using vehicle counts and flows from loop detectors located at the end of every link, and travel time from probe vehicles. From these counts the average travel time is calculated using the "cumulative plot" method. Furthermore, in order to incorporate the GPS data, a map-matching method is used to associate a travel time to the appropriate link.
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paper_title: A vehicle re-identification algorithm based on multi-sensor correlation

paper_content:

Magnetic sensors can be applied in vehicle recognition. Most of the existing vehicle recognition algorithms use one sensor node to measure a vehicle‖s signature. However, vehicle speed variation and environmental disturbances usually cause errors during such a process. In this paper we propose a method using multiple sensor nodes to accomplish vehicle recognition. Based on the matching result of one vehicle‖s signature obtained by different nodes, this method determines vehicle status and corrects signature segmentation. The co-relationship between signatures is also obtained, and the time offset is corrected by such a co-relationship. The corrected signatures are fused via maximum likelihood estimation, so as to obtain more accurate vehicle signatures. Examples show that the proposed algorithm can provide input parameters with higher accuracy. It improves the average accuracy of vehicle recognition from 94.0% to 96.1%, and especially the bus recognition accuracy from 77.6% to 92.8%.
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paper_title: An architecture of traffic state analysis based on multi-sensor fusion

paper_content:

Urban transportation system is a distributed, man-involved and time-variable complex system. So the traffic state analysis is important because it can provide, in a real time way, global view of traffic state in the level of road network, which is very clearly useful in traffic control and route guidance. However, the traffic state analysis involves substantial data and multi-sensor data. So the architecture is important because it can define the mechanism for data management and state analysis. The provided architecture in this paper includes the global data management mechanism, the framework of area traffic state analysis, and the uniform traffic state variable. In global traffic data management, the CORBA-A mechanism combing CORBA, agent and XML for data management is provided, which can deal with the distributed heterogeneous dynamic and substantial traffic data. In the traffic state analysis, the layered framework is provided. The uniform state variable is also defined in the architecture, which is the basis of data fusion for GPS data, loop detector data, map data and other data. The case study shows the provided architecture feasible.
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paper_title: Sensor fusion for surgical applications

paper_content:

The CAMIS prototype surgical navigation system combines preoperative 3D imagery and intra-operative localization to register a patient with this imagery. While this is vastly superior to the exploratory surgery that it replaced, improving anatomical localization is one of the primary goals of the CAMIS project. This presentation discusses the fusion of preoperative and intraoperative ultrasound mensuration imagery. In particular, a PC based ultrasound system developed at Washington University was modified to add localization capabilities. Then using a unique single point calibration procedure, the projection of the ultrasound 2 dimensional coordinates into full 3 dimensional patient coordinates was developed. Thus the 3D coordinates of any point in the ultrasound image can be directly calculated from this mapping. The fusion of preoperative CT imagery with this calibrated real time ultrasound is currently being tested using a spinal phantom. A 3D CT image set of the phantom was acquired and is used as a 3D model for generating a synthetic ultrasound axial image slice. This synthetic ultrasound image was generated using the Ohio University Synthetic Ultrasound code developed under the CAMIS project. Then the spinal phantom was imaged while submerged in water and the acquisition geometry was manually aligned to get the best apparent match between the measured and synthetic ultrasound images. These two images were then computer matched by adjusting the synthetic acquisition parameters until a best match was obtained. A coordinate transformation is then shown that convert from CT coordinates to patient coordinates so that the patient and the CT image are geometrically registered through the intra-operative ultrasound image.
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paper_title: Neural network-based face detection

paper_content:

We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.
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paper_title: Federated Kalman Filter Simulation Results

paper_content:

This paper describes federated filter applications to integrated, fault-tolerant navigation systems, with emphasis on real-time implementation issues and numerical simulation results. The federated filter is a near-optimal estimator for decentralized, multisensor data fusion. Its partitioned estimation architecture is based on theoretically sound information-sharing principles. It consists of one or more sensor-dedicated local filters, generally operating in parallel, plus a master combining filter. The master filter periodically combines (fuses) the local filter solutions to form the best total solution. Fusion generally occurs at a reduced rate, relative to the local measurement rates. The method can provide significant improvements in fault tolerance, data throughput, and system modularity. Numerical simulation results are presented for an example multisensor navigation system. These results demonstrate federated filter performance characteristics in terms of estimation accuracy, fault tolerance, and computation speed.
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paper_title: Multisensor Data Fusion

paper_content:

Expanding the scope of the bestselling first edition, this new edition is now in two volumes. It contains nine new chapters and focuses on the most recent developments in the fusion of data in a variety of applications from military to automotive to medical. It provides information on mathematical techniques and computer methods employed to perform fusion. The set includes new material on target tracking and identification, situation refinement, consequence prediction, resource allocation and refinement, and human computer interaction and cognitive support. It provides new material on engineering issues such as fusion in distributed network systems, information security, and issues with service-oriented architectures.
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paper_title: DEMPSTER-SHAFER DATA FUSION AT THE TRAFFIC MANAGEMENT CENTER

paper_content:

This paper is concerned with the application of Dempster- Shafer inference at a traffic management center (TMC) to support incident detection and the identification of other events of concern to traffic managers. Dempster- Shafer inference, a statistical- based data fusion classification algorithm, is used when the sensors or other data sources contributing information cannot associate a 100 percent probability of certainty to their output decisions. The algorithm captures and combines whatever certainty exists in the object discrimination or event classification capability of the sensors and other sources of information. Knowledge from multiple sources about the events is combined using Dempster's rule to find the intersection or conjunction of the events and the associated probability. The application of the algorithm to incident detection and verification is illustrated with an example consisting of three possible events, where data are supplied from three different types of sources. The available information is combined using Dempster's rule and the most probable event is identified
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paper_title: Kernel Regression for Image Processing and Reconstruction

paper_content:

In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples.
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paper_title: Anonymous Vehicle Reidentification Using Heterogeneous Detection Systems

paper_content:

An innovative feature of this paper is the demonstration of the feasibility of real-time vehicle reidentification algorithm development at a signalized intersection, where different traffic detection technologies were employed at upstream and downstream locations. Previous research by the authors on vehicle reidentification has utilized the same traffic sensors (e.g., conventional square inductive loops) and detectors (e.g., high-speed scanning detector cards) at both locations. In this paper, an opportunity arose for the first time to collect a downstream data set from a temporary installation of a prototype innovative inductive loop sensor known as a ldquobladerdquo sensor in conjunction with conventional inductive loops upstream. At both locations, advanced high-speed scanning detector cards were used. Although the number of vehicles for which data could be collected was small, encouraging results were obtained for vehicle reidentification performance in this system of mixed traffic detection technologies. In future large-scale applications of vehicle reidentification approaches for real-time traffic performance measurement, management, and control, it would be most beneficial and practical if heterogeneous and homogeneous detection systems could be supported. This initial paper yielded many useful insights about this important issue and demonstrated on a small scale the feasibility of vehicle reidentification in a system with heterogeneous detection technologies.
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paper_title: A physical model for acoustic signatures

paper_content:

A physical model is presented to explain the interference phenomenon that gives rise to the material‐dependent signature obtained from an acoustic reflection microscope. An approximate formula is derived for the peak separation of the characteristic response, and it agrees well with the experimental results.
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paper_title: Sensing and Signal Processing for Vehicle Reidentification and Travel Time Estimation

paper_content:

Link travel times are crucial for advanced traveler information systems and traffic management applications. However, current systems for estimating them still have shortcomings that need to be addressed. In this paper, we propose a novel framework for vehicle reidentification via signature matching using signal processing techniques and a travel time estimation algorithm that is robust to potential (and often inevitable) vehicle misidentifications. Individual vehicles are matched between well-separated stations in a road transportation network using signatures captured by embedded roadway sensors. Statistical and multirate signal processing methods are used to develop data-postprocessing algorithms that are critical to the subsequent signature-matching problem, which is formulated using optimal techniques from communication theory. A probabilistic modeling of the generated matching assignments and an unsupervised data-clustering technique are then used to devise a travel time estimation algorithm. The proposed method is tested under a real traffic scenario, and accurate link travel time measures are reported.
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paper_title: A DATA FUSION ALGORITHM FOR ESTIMATING LINK TRAVEL TIME

paper_content:

The growing demand for real-time traffic information brought about various types of traffic collection mechanisms in the area of Intelligent Transport Systems (ITS). There are, however, two procedures in making various traffic data into information. First, a robust information-making process of utilizing data into the representative information for each traffic collection mechanism is required. Second, the integration process of fusing the “estimated” information into the “representative information” for each link out of each source is also required. That is, both data reduction and/or data-to-information process and a higher-level information fusion are required. This article focuses on the development of an information fusion algorithm based on a voting technique, fuzzy regression, and Bayesian pooling technique for estimating dynamic link travel time in congested urban road networks. The algorithm has been proposed and validated using field experimental data—GPS probes and detector data collected over ...
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paper_title: Its Data Fusion

paper_content:

This project has accomplished three significant tasks. First, a state-of-the-art literature review has provided an organizational framework for categorizing the various data fusion projects that have been conducted to date. A popular typology was discussed which situates data fusion technologies in one of three levels, depending on the degree to which sensor data is correlated to provide users with meaningful transit recommendations. The trade-offs that accompany higher-level data fusion efforts--in terms of computing power and memory requirements--were noted. The advantages of multiple-sensor data fusion projects in terms of cost, accuracy, and reliability were also discussed and contrasts were drawn with the traditional deployment of highly accurate, single sensors. Specific techniques of data fusion were described and their possible applications to Intelligent Transportation System (ITS) projects were explored. In fact, this report is one of the first to consider how data fusion technology might be productively applied to the needs of transportation management. A second major component of this report is the description provided of a local data fusion application. This project employs data fusion techniques to correlate input from multiple highway sensors and generate reliable traffic predictions. The resulting information can be displayed for use by commuters as they choose from among various transit options. The architecture of this data fusion system is described in detail. The third component of the project was to create a statistically based algorithm to estimate speed from volume and occupancy measurements. The algorithm presented explicitly accounts for the statistics of the problem and provides a robustness test for the speed estimate.
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paper_title: Analytical Modeling and Sensitivity Analysis for Travel Time Estimation on Signalized Urban Networks

paper_content:

This paper presents a model for estimation of average travel time and its variability on signalized urban networks using cumulative plots. The plots are generated based on the availability of data: a) case-D, for detector data only; b) case-DS, for detector data and signal timings; and c) case-DSS, for detector data, signal timings and saturation flow rate. The performance of the model for different degrees of saturation and different detector detection intervals is consistent for case-DSS and case-DS whereas, for case-D the performance is inconsistent. The sensitivity analysis of the model for case-D indicates that it is sensitive to detection interval and signal timings within the interval. When detection interval is integral multiple of signal cycle then it has low accuracy and low reliability. Whereas, for detection interval around 1.5 times signal cycle both accuracy and reliability are high.
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paper_title: Combining belief functions based on distance of evidence

paper_content:

A modified average method to combine belief function based on distance measures of evidence is proposed. The weight of each body of evidence (BOE) is taken into account. A numerical example is shown to illustrate the use of the proposed method to combine conflicting evidence. Some open issues are discussed in the final section.
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paper_title: Estimation of Travel Time on Urban Networks with Midlink Sources and Sinks

paper_content:

This paper presents a methodology for estimation of average travel time on signalized urban networks by integrating cumulative plots and probe data. This integration aims to reduce the relative deviations in the cumulative plots due to midlink sources and sinks. During undersaturated traffic conditions, the concept of a virtual probe is introduced, and therefore, accurate travel time can be obtained when a real probe is unavailable. For oversaturated traffic conditions, only one probe per travel time estimation interval—360 s or 3% of vehicles traversing the link as a probe—has the potential to provide accurate travel time.
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paper_title: An Approach to Urban Traffic State Estimation by Fusing Multisource Information

paper_content:

This paper presents an information-fusion-based approach to the estimation of urban traffic states. The approach can fuse online data from underground loop detectors and global positioning system (GPS)-equipped probe vehicles to more accurately and completely obtain traffic state estimation than using either of them alone. In this approach, three parts of the algorithms are developed for fusion computing and the data processing of loop detectors and GPS probe vehicles. First, a fusion algorithm, which integrates the federated Kalman filter and evidence theory (ET), is proposed to prepare a robust, credible, and extensible fusion platform for the fusion of multisensor data. After that, a novel algorithm based on the traffic wave theory is employed to estimate the link mean speed using single-loop detectors buried at the end of links. With the GPS data, a series of technologies are combined with the geographic information systems for transportation (GIS-T) map to compute another link mean speed. These two speeds are taken as the inputs of the proposed fusion platform. Finally, tests on the accuracy, conflict resistance, robustness, and operation speed by real-world traffic data illustrate that the proposed approach can well be used in urban traffic applications on a large scale.
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paper_title: Vehicle Classification and Identification Using Multi-Modal Sensing and Signal Learning

paper_content:

Vehicle counting, time-of-travel analysis, and other traffic studies frequently require the classification and identification of vehicles in a roadway. Unfortunately, many current technologies for identifying vehicles, such as image-based methods that use cameras and machine vision, are not appropriate for studies that require low-power consumption and low cost. Additionally, privacy issues are becoming a larger concern with the increasing controversy surrounding the public collection of imagery. In this work we evaluate a multi-modal sensing approach to vehicle classification and identification using an ensemble of sensors measurements including electromagnetic emanations and acoustic signatures. A novel kernel regression method is also used for signal learning to classify and identify vehicles without the need of invasive images. Multi-mode sensing, as well as signal learning, is shown to significantly increase the classification rate of specific vehicle classes.
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paper_title: Classifiers and distance-based evidential fusion for road travel time estimation

paper_content:

This paper addresses the road travel time estimation on an urban axis by classification method based on evidence theory. The travel time (TT) indicator can be used either for traffic management or for drivers' information. The information used to estimate the travel time (induction loop sensor, cameras, probe vehicle,...) is complementary and redundant. It is then necessary to implement strategies of multi-sensors data fusion. The selected framework is the evidence theory. This theory takes more into account the imprecision and uncertainty of multisource information. Two strategies were implemented. The first one is classifier fusion where each information source, was considered as a classifier. The second approach is a distance-based classification for belief functions modelling. Results of these approaches, on data collected on an urban axis in the South of France, show the outperformance of fusion strategies within this application.
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paper_title: Mathematical Techniques in Multisensor Data Fusion

paper_content:

From the Publisher: ::: This invaluable reference offers the most comprehensive introduction available to the concepts of multisensor data fusion. It introduces key algorithms, provides advice on their utilization, and raises issues associated with their implementation. With a diverse set of mathematical and heuristic techniques for combining data from multiple sources, the book shows how to implement a data fusion system, describes the process for algorithm selection, functional architectures and requirements for ancillary software, and illustrates man-machine interface requirements an database issues.
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paper_title: A pattern recognition and feature fusion formulation for vehicle reidentification in Intelligent Transportation Systems

paper_content:

Vehicle reidentification is the process of reidentifying or tracking vehicles from one point on the roadway to the next. By performing vehicle reidentification, important traffic parameters including travel time, section density and partial dynamic origin/destination demands can be obtained. This provides for anonymous tracking of vehicles from site-to-site and has the potential for improving Intelligent Transportation Systems (ITS) by providing more accurate data. This paper presents a new vehicle reidentification algorithm that uses four different features, namely, (I) the inductive signature vector acquired from loop detectors, (2) vehicle velocity, (3) traversal time and (4) color information (based on images acquired from video cameras) to achieve high accuracy. A nearest neighbor approach classifies the features and linear feature fusion is shown to improve performance. With the fusion of four features, more than a 91 percent accuracy is obtained on real data collected from a parkway in California.
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paper_title: Vehicle Reidentification using multidetector fusion

paper_content:

Vehicle reidentification is the process of matching vehicles from one point on the roadway (one field of view) to the next. By performing vehicle reidentification, important traffic parameters including travel time, travel time variability, section density, and partial dynamic origin/destination demands can be obtained. Field traffic data were collected in Alton Parkway in Southern California for training and testing of the multidetector vehicle reidentification algorithm. These data consisted of inductive loop signatures of vehicles that traversed two detector stations spanning a section of an arterial and the corresponding video of these signatures. Even though the video collected was not optimized for pattern-recognition purposes, an investigation into the feasibility of fusing inductive vehicle signatures with video for anonymous vehicle reidentification was conducted. The resulting reidentification rate of over 90% shows that this approach merits further investigation. The results also show that the use of detector fusion provides system redundancy and yields slightly better results than the use of a single detector.
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paper_title: Fusion of wavelet transform and color information features for automatic vehicle reidentification in intelligent transportation systems

paper_content:

Vehicle reidentification is the process of reidentifying or tracking vehicles from one point on the roadway to the next. By performing vehicle reidentification, important traffic parameters including travel time, section density and partial dynamic origin/destination demands can be obtained. This provides for anonymous tracking of vehicles from site-to-site and has the potential for improving intelligent transportation systems (ITS) by providing more accurate data. The paper presents a fusion based vehicle reidentification algorithm that uses four different features to achieve high accuracy: (1) the wavelet transform of the inductive signature vector acquired from loop detectors; (2) vehicle velocity; (3) traversal time; (4) color information (based on images acquired from video cameras). A nearest neighbor approach classifies the features and linear feature fusion is shown to improve performance. With the fusion of four features, more than a 92% accuracy is obtained on real data collected from a parkway in California. Also, it is found that the wavelet transform improves performance and reduces the dimension of the feature vector when compared to the raw vehicle signatures.
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paper_title: Intelligent multi-camera video surveillance: A review

paper_content:

Intelligent multi-camera video surveillance is a multidisciplinary field related to computer vision, pattern recognition, signal processing, communication, embedded computing and image sensors. This paper reviews the recent development of relevant technologies from the perspectives of computer vision and pattern recognition. The covered topics include multi-camera calibration, computing the topology of camera networks, multi-camera tracking, object re-identification, multi-camera activity analysis and cooperative video surveillance both with active and static cameras. Detailed descriptions of their technical challenges and comparison of different solutions are provided. It emphasizes the connection and integration of different modules in various environments and application scenarios. According to the most recent works, some problems can be jointly solved in order to improve the efficiency and accuracy. With the fast development of surveillance systems, the scales and complexities of camera networks are increasing and the monitored environments are becoming more and more complicated and crowded. This paper discusses how to face these emerging challenges.
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paper_title: Car re-identification from large scale images using semantic attributes

paper_content:

Car re-identification, searching a specific car object from a large-scale car image database, is investigated in this paper. Previous work mainly focuses on fixed pose and overlooks the special appearance. However, avoiding matching other poses would lead to coarse results of the car retrieval. And some special attributes like individual paintings which are greatly helpful for car retrieval have not drawn enough attention. This paper addresses these problems through multi-poses matching and re-ranking based on special attributes. Our core idea lies in query expansion method that can capture weighted attributes to build the retrieval model, which allows us to estimate invisible attributes by the visible ones to construct complete attributes vectors to car retrieval in any poses. Furthermore, we divide all attributes into two groups, special attributes and common attributes. Here special attributes represent the abnormal appearance like individual paintings or car damage while common attributes denote the intrinsic appearance of car. Using special attributes to re-rank results turns out to be beneficial to improve the retrieval performance. In the end, the experiments demonstrate the effectiveness of our approach on the car datasets.
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paper_title: Vehicle matching and recognition under large variations of pose and illumination

paper_content:

Matching vehicles subject to both large pose transformations and extreme illumination variations remains a technically challenging problem in computer vision. In this paper, we develop a new and robust framework toward matching and recognizing vehicles with both highly varying poses and drastically changing illumination conditions. By effectively estimating both pose and illumination conditions, we can re-render vehicles in the reference image to generate the relit image with the same pose and illumination conditions as the target image. We compare the relit image and the re-rendered target image to match vehicles in the original reference image and target image. Furthermore, no training is needed in our framework and re-rendered vehicle images in any other viewpoints and illumination conditions can be obtained from just one single input image. Experimental results demonstrate the robustness and efficacy of our framework, with a potential to generalize our current method from vehicles to handle other types of objects.
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paper_title: Vehicle Re-identification for Automatic Video Traffic Surveillance

paper_content:

This paper proposes an approach to the vehicle reidentification problem in a multiple camera system. We focused on the re-identification itself assuming that the vehicle detection problem is already solved including extraction of a full-fledged 3D bounding box. The re-identification problem is solved by using color histograms and histograms of oriented gradients by a linear regressor. The features are used in separate models in order to get the best results in the shortest CPU computation time. The proposed method works with a high accuracy (60% true positives retrieved with 10% false positive rate on a challenging subset of the test data) in 85 milliseconds of the CPU (Core i7) computation time per one vehicle re-identification assuming the fullHD resolution video input. The applications of this work include finding important parameters such as travel time, traffic flow, or traffic information in a distributed traffic surveillance and monitoring system.
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paper_title: Building a classification cascade for visual identification from one example

paper_content:

Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). Two special challenges characterize OID. (1) Interclass variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. (2) There may be many classes but few or just one positive "training" examples per class. Due to (1), a solution must locate possibly subtle object-specific salient features (a door handle) while avoiding distracting ones (a specular highlight). However, (2) rules out direct techniques of feature selection. We describe an online algorithm that takes one model image from a known category and builds an efficient "same" vs. "different" classification cascade by predicting the most discriminative feature set for that object. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered feature sequence unique to a specific model image, maximizing cumulative information content. Learned stopping thresholds make the classifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category, without prior knowledge about the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.
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paper_title: License Plate Recognition From Still Images and Video Sequences: A Survey

paper_content:

License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.
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paper_title: Vehicle identification between non-overlapping cameras without direct feature matching

paper_content:

We propose a novel method for identifying road vehicles between two nonoverlapping cameras. The problem is formulated as a same-different classification problem: probability of two vehicle images from two distinct cameras being from the same vehicle or from different vehicles. The key idea is to compute the probability without matching the two vehicle images directly, which is a process vulnerable to drastic appearance and aspect changes. We represent each vehicle image as an embedding amongst representative exemplars of vehicles within the same camera. The embedding is computed as a vector each of whose components is a nonmetric distance for a vehicle to an exemplar. The nonmetric distances are computed using robust matching of oriented edge images. A set of truthed training examples of same-different vehicle pairings across the two cameras is used to learn a classifier that encodes the probability distributions. A pair of the embeddings representing two vehicles across two cameras is then used to compute the same-different probability. In order for the vehicle exemplars to be representative for both cameras, we also propose a method for jointly selection of corresponding exemplars using the training data. Experiments on observations of over 400 vehicles under drastically illumination and camera conditions demonstrate promising results.
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paper_title: Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos

paper_content:

We present a novel approach for visual detection and attribute-based search of vehicles in crowded surveillance scenes. Large-scale processing is addressed along two dimensions: 1) large-scale indexing, where hundreds of billions of events need to be archived per month to enable effective search and 2) learning vehicle detectors with large-scale feature selection, using a feature pool containing millions of feature descriptors. Our method for vehicle detection also explicitly models occlusions and multiple vehicle types (e.g., buses, trucks, SUVs, cars), while requiring very few manual labeling. It runs quite efficiently at an average of 66 Hz on a conventional laptop computer. Once a vehicle is detected and tracked over the video, fine-grained attributes are extracted and ingested into a database to allow future search queries such as “Show me all blue trucks larger than 7 ft. length traveling at high speed northbound last Saturday, from 2 pm to 5 pm”. We perform a comprehensive quantitative analysis to validate our approach, showing its usefulness in realistic urban surveillance settings.
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paper_title: Matching vehicles under large pose transformations using approximate 3D models and piecewise MRF model

paper_content:

We propose a robust object recognition method based on approximate 3D models that can effectively match objects under large viewpoint changes and partial occlusion. The specific problem we solve is: given two views of an object, determine if the views are for the same or different object. Our domain of interest is vehicles, but the approach can be generalized to other man-made rigid objects. A key contribution of our approach is the use of approximate models with locally and globally constrained rendering to determine matching objects. We utilize a compact set of 3D models to provide geometry constraints and transfer appearance features for object matching across disparate viewpoints. The closest model from the set, together with its poses with respect to the data, is used to render an object both at pixel (local) level and region/part (global) level. Especially, symmetry and semantic part ownership are used to extrapolate appearance information. A piecewise Markov Random Field (MRF) model is employed to combine observations obtained from local pixel and global region level. Belief Propagation (BP) with reduced memory requirement is employed to solve the MRF model effectively. No training is required, and a realistic object image in a disparate viewpoint can be obtained from as few as just one image. Experimental results on vehicle data from multiple sensor platforms demonstrate the efficacy of our method.
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paper_title: Approximate license plate string matching for vehicle re-identification

paper_content:

From the empirical studies, it is quite difficult for the license plate recognition to perform 100% accuracy in a real world environment. Nevertheless, it is common that only a few characters are misread from a license plate recognition system. In this paper, license plate matching is used for vehicle re-identification. We evaluate several approximate string matching techniques to determine an applicable method for the domain of license plate matching. We also propose a weighted approximate string matching to further improve the performance. Our experimental results show that the generalized edit distance (GED) yields highest precision while the 2-Grams yields the highest recall. By weighting the distance cost from those two methods, our proposed weighted method gives the best performance.
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paper_title: Unsupervised Learning of Discriminative Edge Measures for Vehicle Matching between Nonoverlapping Cameras

paper_content:

This paper proposes a novel unsupervised algorithm learning discriminative features in the context of matching road vehicles between two nonoverlapping cameras. The matching problem is formulated as a same-different classification problem; which aims to compute the probability of vehicle images from two distinct cameras being from the same vehicle or different vehicle(s). We employ a novel measurement vector that consists of three independent edge-based measures and their associated robust measures computed from a pair of aligned vehicle edge maps. The weight of each measure is determined by an unsupervised learning algorithm that optimally separates the same-different classes in the combined measurement space. This is achieved with a weak classification algorithm that automatically collects representative samples from same-different classes, followed by a more discriminative classifier based on Fisher's linear discriminants and Gibbs sampling. The robustness of the match measures and the use of unsupervised discriminant analysis in the classification ensures that the proposed method performs consistently in the presence of missing/false features, temporally and spatially changing illumination conditions and systematic misalignment caused by different camera configurations. Extensive experiments based on real data of more than 200 vehicles at different times of the day demonstrate promising results.
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paper_title: Large-scale vehicle re-identification in urban surveillance videos

paper_content:

Vehicle, as a significant object class in urban surveillance, attracts massive focuses in computer vision field, such as detection, tracking, and classification. Among them, vehicle re-identification (Re-Id) is an important yet frontier topic, which not only faces the challenges of enormous intra-class and subtle inter-class differences of vehicles in multicameras, but also suffers from the complicated environments in urban surveillance scenarios. Besides, the existing vehicle related datasets all neglect the requirements of vehicle Re-Id: 1) massive vehicles captured in real-world traffic environment; and 2) applicable recurrence rate to give cross-camera vehicle search for vehicle Re-Id. To facilitate vehicle Re-Id research, we propose a large-scale benchmark dataset for vehicle Re-Id in the real-world urban surveillance scenario, named “VeRi”. It contains over 40,000 bounding boxes of 619 vehicles captured by 20 cameras in unconstrained traffic scene. Moreover, each vehicle is captured by 2∼18 cameras in different viewpoints, illuminations, and resolutions to provide high recurrence rate for vehicle Re-Id. Finally, we evaluate six competitive vehicle Re-Id methods on VeRi and propose a baseline which combines the color, texture, and highlevel semantic information extracted by deep neural network.
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paper_title: Group-Sensitive Triplet Embedding for Vehicle Reidentification

paper_content:

The widespread use of surveillance cameras toward smart and safe cities poses the critical but challenging problem of vehicle reidentification (Re-ID). The state-of-the-art research work performed vehicle Re-ID relying on deep metric learning with a triplet network. However, most existing methods basically ignore the impact of intraclass variance-incorporated embedding on the performance of vehicle reidentification, in which robust fine-grained features for large-scale vehicle Re-ID have not been fully studied. In this paper, we propose a deep metric learning method, group-sensitive-triplet embedding (GS-TRE), to recognize and retrieve vehicles, in which intraclass variance is elegantly modeled by incorporating an intermediate representation “group” between samples and each individual vehicle in the triplet network learning. To capture the intraclass variance attributes of each individual vehicle, we utilize an online grouping method to partition samples within each vehicle ID into a few groups, and build up the triplet samples at multiple granularities across different vehicle IDs as well as different groups within the same vehicle ID to learn fine-grained features. In particular, we construct a large-scale vehicle database “PKU-Vehicle,” consisting of 10 million vehicle images captured by different surveillance cameras in several cities, to evaluate the vehicle Re-ID performance in real-world video surveillance applications. Extensive experiments over benchmark datasets VehicleID, VeRI, and CompCar have shown that the proposed GS-TRE significantly outperforms the state-of-the-art approaches for vehicle Re-ID.
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paper_title: Learning a similarity metric discriminatively, with application to face verification

paper_content:

We present a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large and not known during training, and where the number of training samples for a single category is very small. The idea is to learn a function that maps input patterns into a target space such that the L/sub 1/ norm in the target space approximates the "semantic" distance in the input space. The method is applied to a face verification task. The learning process minimizes a discriminative loss function that drives the similarity metric to be small for pairs of faces from the same person, and large for pairs from different persons. The mapping from raw to the target space is a convolutional network whose architecture is designed for robustness to geometric distortions. The system is tested on the Purdue/AR face database which has a very high degree of variability in the pose, lighting, expression, position, and artificial occlusions such as dark glasses and obscuring scarves.
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paper_title: A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance

paper_content:

While re-identification (Re-Id) of persons has attracted intensive attention, vehicle, which is a significant object class in urban video surveillance, is often overlooked by vision community. Most existing methods for vehicle Re-Id only achieve limited performance, as they predominantly focus on the generic appearance of vehicle while neglecting some unique identities of vehicle (e.g., license plate). In this paper, we propose a novel deep learning-based approach to PROgressive Vehicle re-ID, called “PROVID”. Our approach treats vehicle Re-Id as two specific progressive search processes: coarse-to-fine search in the feature space, and near-to-distant search in the real world surveillance environment. The first search process employs the appearance attributes of vehicle for a coarse filtering, and then exploits the Siamese Neural Network for license plate verification to accurately identify vehicles. The near-to-distant search process retrieves vehicles in a manner like human beings, by searching from near to faraway cameras and from close to distant time. Moreover, to facilitate progressive vehicle Re-Id research, we collect to-date the largest dataset named VeRi-776 from large-scale urban surveillance videos, which contains not only massive vehicles with diverse attributes and high recurrence rate, but also sufficient license plates and spatiotemporal labels. A comprehensive evaluation on the VeRi-776 shows that our approach outperforms the state-of-the-art methods by 9.28 % improvements in term of mAP.
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paper_title: Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification

paper_content:

In this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation invariant feature embedding, local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined. With spatial-temporal regularization, the log-normal distribution is adopted to model the spatial-temporal constraints and the retrieval results can be refined. Experiments are conducted on public vehicle ReID datasets and our proposed method achieves state-of-the-art performance. Investigations of the proposed framework is conducted, including the landmark regressor and comparisons with attention mechanism. Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements.
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paper_title: Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals

paper_content:

Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation. It gains increasing attention because of the recent advances of person re-identification techniques. However, unlike person re-identification, the visual differences between pairs of vehicle images are usually subtle and even challenging for humans to distinguish. Incorporating additional spatio-temporal information is vital for solving the challenging re-identification task. Existing vehicle re-identification methods ignored or used over-simplified models for the spatio-temporal relations between vehicle images. In this paper, we propose a two-stage framework that incorporates complex spatio-temporal information for effectively regularizing the re-identification results. Given a pair of vehicle images with their spatio-temporal information, a candidate visual-spatio-temporal path is first generated by a chain MRF model with a deeply learned potential function, where each visual-spatio-temporal state corresponds to an actual vehicle image with its spatio-temporal information. A Siamese-CNN+Path-LSTM model takes the candidate path as well as the pairwise queries to generate their similarity score. Extensive experiments and analysis show the effectiveness of our proposed method and individual components.
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paper_title: Deep Relative Distance Learning: Tell the Difference between Similar Vehicles

paper_content:

The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from a large-scale image or video database. However, compared with person re-identification or face recognition, vehicle search problem has long been neglected by researchers in vision community. This paper focuses on an interesting but challenging problem, vehicle re-identification (a.k.a precise vehicle search). We propose a Deep Relative Distance Learning (DRDL) method which exploits a two-branch deep convolutional network to project raw vehicle images into an Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. To further facilitate the future research on this problem, we also present a carefully-organized largescale image database "VehicleID", which includes multiple images of the same vehicle captured by different realworld cameras in a city. We evaluate our DRDL method on our VehicleID dataset and another recently-released vehicle model classification dataset "CompCars" in three sets of experiments: vehicle re-identification, vehicle model verification and vehicle retrieval. Experimental results show that our method can achieve promising results and outperforms several state-of-the-art approaches.
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paper_title: A large-scale car dataset for fine-grained categorization and verification

paper_content:

This paper aims to highlight vision related tasks centered around “car”, which has been largely neglected by vision community in comparison to other objects. We show that there are still many interesting car-related problems and applications, which are not yet well explored and researched. To facilitate future car-related research, in this paper we present our on-going effort in collecting a large-scale dataset, “CompCars”, that covers not only different car views, but also their different internal and external parts, and rich attributes. Importantly, the dataset is constructed with a cross-modality nature, containing a surveillance-nature set and a web-nature set. We further demonstrate a few important applications exploiting the dataset, namely car model classification, car model verification, and attribute prediction. We also discuss specific challenges of the car-related problems and other potential applications that worth further investigations. The latest dataset can be downloaded at http://mmlab.ie.cuhk.edu.hk/ datasets/comp_cars/index.html
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paper_title: Multi-modal metric learning for vehicle re-identification in traffic surveillance environment

paper_content:

Vehicle re-identification (Re-Id) aims to retrieve the same vehicle captured by disjoint cameras at different time instants from different locations, and is a challenging task mainly due to the high similarity among the captured vehicle images in surveillance environment. With the rapid development of Convolutional Neural Network (CNN), learning-based deep features have been adopted to combine with hand-crafted features to re-identify vehicles in traffic surveillance environment. However, the two kinds of features are in different feature space, and if they are fused directly together, their complementary correlation is not able to be fully explored. To address such an issue, this paper proposes a multi-modal metric learning architecture to fuse deep features and hand-crafted ones in an end-to-end optimization network, which achieves a more robust and discriminative feature representation for vehicle re-identification. The extensive experiments on a large-scale traffic surveillance vehicle dataset demonstrate that our proposed approach substantially outperforms the state-of-the-art methods on vehicle Re-Id.
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paper_title: Vehicle re-identification by fusing multiple deep neural networks

paper_content:

Vehicle re-identification has become a fundamental task because of the growing explosion in the use of surveillance cameras in public security. The most widely used solution is based on license plate verification. But when facing the vehicle without a license, deck cars and other license plate information error or missing situation, vehicle searching is still a challenging problem. This paper proposed a vehicle re-identification method based on deep learning which exploit a two-branch Multi-DNN Fusion Siamese Neural Network (MFSNN) to fuses the classification outputs of color, model and pasted marks on the windshield and map them into a Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. In order to achieve this goal, we present a method of vehicle color identification based on Alex net, a method of vehicle model identification based on VGG net, a method of pasted marks detection and identification based on Faster R-CNN. We evaluate our MFSNN method on VehicleID dataset and in the experiment. Experiment results show that our method can achieve promising results.
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paper_title: Car make and model recognition using 3D curve alignment

paper_content:

We present a new approach for recognizing the make and model of a car from a single image. While most previous methods are restricted to fixed or limited viewpoints, our system is able to verify a car's make and model from an arbitrary view. Our model consists of 3D space curves obtained by backprojecting image curves onto silhouette-based visual hulls and then refining them using three-view curve matching. These 3D curves are then matched to 2D image curves using a 3D view-based alignment technique. We present two different methods for estimating the pose of a car, which we then use to initialize the 3D curve matching. Our approach is able to verify the exact make and model of a car over a wide range of viewpoints in cluttered scenes.
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paper_title: Null Foley-Sammon transform

paper_content:

In this paper, an important conclusion for linear discriminant analysis is proved: in the case of the small sample size, (number-of-classes-1) null projection directions (NPDs) do exist, on which, the within-class distance equals zero and the between-class distance is positive. The Null Foley-Sammon Transform (NFST), which is constituted by NPDs, is also proposed. Its effectiveness is evidenced by experiments on face recognition.
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paper_title: Data-driven 3D Voxel Patterns for object category recognition

paper_content:

Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image. In this paper, we propose a novel object representation, 3D Voxel Pattern (3DVP), that jointly encodes the key properties of objects including appearance, 3D shape, viewpoint, occlusion and truncation. We discover 3DVPs in a data-driven way, and train a bank of specialized detectors for a dictionary of 3DVPs. The 3DVP detectors are capable of detecting objects with specific visibility patterns and transferring the meta-data from the 3DVPs to the detected objects, such as 2D segmentation mask, 3D pose as well as occlusion or truncation boundaries. The transferred meta-data allows us to infer the occlusion relationship among objects, which in turn provides improved object recognition results. Experiments are conducted on the KITTI detection benchmark [17] and the outdoor-scene dataset [41]. We improve state-of-the-art results on car detection and pose estimation with notable margins (6% in difficult data of KITTI). We also verify the ability of our method in accurately segmenting objects from the background and localizing them in 3D.
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paper_title: Deep Metric Learning via Lifted Structured Feature Embedding

paper_content:

Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolutional neural networks, recent works have shown promising results on discriminatively training the networks to learn semantic feature embeddings where similar examples are mapped close to each other and dissimilar examples are mapped farther apart. In this paper, we describe an algorithm for taking full advantage of the training batches in the neural network training by lifting the vector of pairwise distances within the batch to the matrix of pairwise distances. This step enables the algorithm to learn the state of the art feature embedding by optimizing a novel structured prediction objective on the lifted problem. Additionally, we collected Online Products dataset: 120k images of 23k classes of online products for metric learning. Our experiments on the CUB-200-2011, CARS196, and Online Products datasets demonstrate significant improvement over existing deep feature embedding methods on all experimented embedding sizes with the GoogLeNet network.
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paper_title: Vehicle Color Recognition With Spatial Pyramid Deep Learning

paper_content:

Color, as a notable and stable attribute of vehicles, can serve as a useful and reliable cue in a variety of applications in intelligent transportation systems. Therefore, vehicle color recognition in natural scenes has become an important research topic in this area. In this paper, we propose a deep-learning-based algorithm for automatic vehicle color recognition. Different from conventional methods, which usually adopt manually designed features, the proposed algorithm is able to adaptively learn representation that is more effective for the task of vehicle color recognition, which leads to higher recognition accuracy and avoids preprocessing. Moreover, we combine the widely used spatial pyramid strategy with the original convolutional neural network architecture, which further boosts the recognition accuracy. To the best of our knowledge, this is the first work that employs deep learning in the context of vehicle color recognition. The experiments demonstrate that the proposed approach achieves superior performance over conventional methods.
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paper_title: Very Deep Convolutional Networks for Large-Scale Image Recognition

paper_content:

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.
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paper_title: Deep joint discriminative learning for vehicle re-identification and retrieval

paper_content:

In this paper, we propose a novel vehicle re-identification method based on a Deep Joint Discriminative Learning (DJDL) model, which utilizes a deep convolutional network to effectively extract discriminative representations for vehicle images. To exploit properties and relationship among samples in different views, we design a unified framework to combine several different tasks efficiently, including identification, attribute recognition, verification and triplet tasks. The whole network is optimized jointly via a specific batch composition design. Extensive experiments are conducted on a large-scale VehicleID [1] dataset. Experimental results demonstrate the effectiveness of our method and show that it achieves the state-of-the-art performance on both vehicle re-identification and retrieval.
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paper_title: Siamese neural network based gait recognition for human identification

paper_content:

As the remarkable characteristics of remote accessed, robust and security, gait recognition has gained significant attention in the biometrics based human identification task. However, the existed methods mainly employ the handcrafted gait features, which cannot well handle the indistinctive inter-class differences and large intra-class variations of human gait in real-world situation. In this paper, we have developed a Siamese neural network based gait recognition framework to automatically extract robust and discriminative gait features for human identification. Different from conventional deep neural network, the Siamese network can employ distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for pairs from different persons. In particular, to further learn effective model with limited training data, we composite the gait energy images instead of raw sequence of gaits. Consequently, the experiments on the world's largest gait database show our framework impressively outperforms state-of-the-arts.
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paper_title: Large-scale vehicle re-identification in urban surveillance videos

paper_content:

Vehicle, as a significant object class in urban surveillance, attracts massive focuses in computer vision field, such as detection, tracking, and classification. Among them, vehicle re-identification (Re-Id) is an important yet frontier topic, which not only faces the challenges of enormous intra-class and subtle inter-class differences of vehicles in multicameras, but also suffers from the complicated environments in urban surveillance scenarios. Besides, the existing vehicle related datasets all neglect the requirements of vehicle Re-Id: 1) massive vehicles captured in real-world traffic environment; and 2) applicable recurrence rate to give cross-camera vehicle search for vehicle Re-Id. To facilitate vehicle Re-Id research, we propose a large-scale benchmark dataset for vehicle Re-Id in the real-world urban surveillance scenario, named “VeRi”. It contains over 40,000 bounding boxes of 619 vehicles captured by 20 cameras in unconstrained traffic scene. Moreover, each vehicle is captured by 2∼18 cameras in different viewpoints, illuminations, and resolutions to provide high recurrence rate for vehicle Re-Id. Finally, we evaluate six competitive vehicle Re-Id methods on VeRi and propose a baseline which combines the color, texture, and highlevel semantic information extracted by deep neural network.
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paper_title: Vehicle Re-Identification by Deep Hidden Multi-View Inference

paper_content:

Vehicle re-identification (re-ID) is an area that has received far less attention in the computer vision community than the prevalent person re-ID. Possible reasons for this slow progress are the lack of appropriate research data and the special 3D structure of a vehicle. Previous works have generally focused on some specific views (e.g., front); but, these methods are less effective in realistic scenarios, where vehicles usually appear in arbitrary views to cameras. In this paper, we focus on the uncertainty of vehicle viewpoint in re-ID, proposing two end-to-end deep architectures: the Spatially Concatenated ConvNet and convolutional neural network (CNN)-LSTM bi-directional loop. Our models exploit the great advantages of the CNN and long short-term memory (LSTM) to learn transformations across different viewpoints of vehicles. Thus, a multi-view vehicle representation containing all viewpoints' information can be inferred from the only one input view, and then used for learning to measure distance. To verify our models, we also introduce a Toy Car RE-ID data set with images from multiple viewpoints of 200 vehicles. We evaluate our proposed methods on the Toy Car RE-ID data set and the public Multi-View Car, VehicleID, and VeRi data sets. Experimental results illustrate that our models achieve consistent improvements over the state-of-the-art vehicle re-ID approaches.
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paper_title: Improving triplet-wise training of convolutional neural network for vehicle re-identification

paper_content:

Vehicle re-identification (re-id) plays an important role in the automatic analysis of the drastically increasing urban surveillance videos. Similar to the other image retrieval problems, vehicle re-id suffers from the difficulties caused by various poses of vehicles, diversified illuminations, and complicated environments. Triplet-wise training of convolutional neural network (CNN) has been studied to address these challenges, where the CNN is adopted to automate the feature extraction from images, and the training adopts triplets of (query, positive example, negative example) to capture the relative similarity between them to learn representative features. The traditional triplet-wise training is weakly constrained and thus fails to achieve satisfactory results. We propose to improve the triplet-wise training at two aspects: first, a stronger constraint namely classification-oriented loss is augmented with the original triplet loss; second, a new triplet sampling method based on pairwise images is designed. Our experimental results demonstrate the effectiveness of the proposed methods that achieve superior performance than the state-of-the-arts on two vehicle re-id datasets, which are derived from real-world urban surveillance videos.
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paper_title: PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance

paper_content:

Compared with person reidentification, which has attracted concentrated attention, vehicle reidentification is an important yet frontier problem in video surveillance and has been neglected by the multimedia and vision communities. Since most existing approaches mainly consider the general vehicle appearance for reidentification while overlooking the distinct vehicle identifier, such as the license plate number, they attain suboptimal performance. In this paper, we propose PROVID, a PROgressive Vehicle re-IDentification framework based on deep neural networks. In particular, our framework not only utilizes the multimodality data in large-scale video surveillance, such as visual features, license plates, camera locations, and contextual information, but also considers vehicle reidentification in two progressive procedures: coarse-to-fine search in the feature domain, and near-to-distant search in the physical space. Furthermore, to evaluate our progressive search framework and facilitate related research, we construct the VeRi dataset, which is the most comprehensive dataset from real-world surveillance videos. It not only provides large numbers of vehicles with varied labels and sufficient cross-camera recurrences but also contains license plate numbers and contextual information. Extensive experiments on the VeRi dataset demonstrate both the accuracy and efficiency of our progressive vehicle reidentification framework.
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paper_title: A large-scale car dataset for fine-grained categorization and verification

paper_content:

This paper aims to highlight vision related tasks centered around “car”, which has been largely neglected by vision community in comparison to other objects. We show that there are still many interesting car-related problems and applications, which are not yet well explored and researched. To facilitate future car-related research, in this paper we present our on-going effort in collecting a large-scale dataset, “CompCars”, that covers not only different car views, but also their different internal and external parts, and rich attributes. Importantly, the dataset is constructed with a cross-modality nature, containing a surveillance-nature set and a web-nature set. We further demonstrate a few important applications exploiting the dataset, namely car model classification, car model verification, and attribute prediction. We also discuss specific challenges of the car-related problems and other potential applications that worth further investigations. The latest dataset can be downloaded at http://mmlab.ie.cuhk.edu.hk/ datasets/comp_cars/index.html
---
paper_title: Large-scale vehicle re-identification in urban surveillance videos

paper_content:

Vehicle, as a significant object class in urban surveillance, attracts massive focuses in computer vision field, such as detection, tracking, and classification. Among them, vehicle re-identification (Re-Id) is an important yet frontier topic, which not only faces the challenges of enormous intra-class and subtle inter-class differences of vehicles in multicameras, but also suffers from the complicated environments in urban surveillance scenarios. Besides, the existing vehicle related datasets all neglect the requirements of vehicle Re-Id: 1) massive vehicles captured in real-world traffic environment; and 2) applicable recurrence rate to give cross-camera vehicle search for vehicle Re-Id. To facilitate vehicle Re-Id research, we propose a large-scale benchmark dataset for vehicle Re-Id in the real-world urban surveillance scenario, named “VeRi”. It contains over 40,000 bounding boxes of 619 vehicles captured by 20 cameras in unconstrained traffic scene. Moreover, each vehicle is captured by 2∼18 cameras in different viewpoints, illuminations, and resolutions to provide high recurrence rate for vehicle Re-Id. Finally, we evaluate six competitive vehicle Re-Id methods on VeRi and propose a baseline which combines the color, texture, and highlevel semantic information extracted by deep neural network.
---
paper_title: Vehicle Re-Identification by Deep Hidden Multi-View Inference

paper_content:

Vehicle re-identification (re-ID) is an area that has received far less attention in the computer vision community than the prevalent person re-ID. Possible reasons for this slow progress are the lack of appropriate research data and the special 3D structure of a vehicle. Previous works have generally focused on some specific views (e.g., front); but, these methods are less effective in realistic scenarios, where vehicles usually appear in arbitrary views to cameras. In this paper, we focus on the uncertainty of vehicle viewpoint in re-ID, proposing two end-to-end deep architectures: the Spatially Concatenated ConvNet and convolutional neural network (CNN)-LSTM bi-directional loop. Our models exploit the great advantages of the CNN and long short-term memory (LSTM) to learn transformations across different viewpoints of vehicles. Thus, a multi-view vehicle representation containing all viewpoints' information can be inferred from the only one input view, and then used for learning to measure distance. To verify our models, we also introduce a Toy Car RE-ID data set with images from multiple viewpoints of 200 vehicles. We evaluate our proposed methods on the Toy Car RE-ID data set and the public Multi-View Car, VehicleID, and VeRi data sets. Experimental results illustrate that our models achieve consistent improvements over the state-of-the-art vehicle re-ID approaches.
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paper_title: Exploiting Multi-grain Ranking Constraints for Precisely Searching Visually-similar Vehicles

paper_content:

Precise search of visually-similar vehicles poses a great challenge in computer vision, which needs to find exactly the same vehicle among a massive vehicles with visually similar appearances for a given query image. In this paper, we model the relationship of vehicle images as multiple grains. Following this, we propose two approaches to alleviate the precise vehicle search problem by exploiting multi-grain ranking constraints. One is Generalized Pairwise Ranking, which generalizes the conventional pairwise from considering only binary similar/dissimilar relations to multiple relations. The other is Multi-Grain based List Ranking, which introduces permutation probability to score a permutation of a multi-grain list, and further optimizes the ranking by the likelihood loss function. We implement the two approaches with multi-attribute classification in a multi-task deep learning framework. To further facilitate the research on precise vehicle search, we also contribute two high-quality and well-annotated vehicle datasets, named VD1 and VD2, which are collected from two different cities with diverse annotated attributes. As two of the largest publicly available precise vehicle search datasets, they contain 1,097,649 and 807,260 vehicle images respectively. Experimental results show that our approaches achieve the state-of-the-art performance on both datasets.
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paper_title: Group-Sensitive Triplet Embedding for Vehicle Reidentification

paper_content:

The widespread use of surveillance cameras toward smart and safe cities poses the critical but challenging problem of vehicle reidentification (Re-ID). The state-of-the-art research work performed vehicle Re-ID relying on deep metric learning with a triplet network. However, most existing methods basically ignore the impact of intraclass variance-incorporated embedding on the performance of vehicle reidentification, in which robust fine-grained features for large-scale vehicle Re-ID have not been fully studied. In this paper, we propose a deep metric learning method, group-sensitive-triplet embedding (GS-TRE), to recognize and retrieve vehicles, in which intraclass variance is elegantly modeled by incorporating an intermediate representation “group” between samples and each individual vehicle in the triplet network learning. To capture the intraclass variance attributes of each individual vehicle, we utilize an online grouping method to partition samples within each vehicle ID into a few groups, and build up the triplet samples at multiple granularities across different vehicle IDs as well as different groups within the same vehicle ID to learn fine-grained features. In particular, we construct a large-scale vehicle database “PKU-Vehicle,” consisting of 10 million vehicle images captured by different surveillance cameras in several cities, to evaluate the vehicle Re-ID performance in real-world video surveillance applications. Extensive experiments over benchmark datasets VehicleID, VeRI, and CompCar have shown that the proposed GS-TRE significantly outperforms the state-of-the-art approaches for vehicle Re-ID.
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paper_title: Car re-identification from large scale images using semantic attributes

paper_content:

Car re-identification, searching a specific car object from a large-scale car image database, is investigated in this paper. Previous work mainly focuses on fixed pose and overlooks the special appearance. However, avoiding matching other poses would lead to coarse results of the car retrieval. And some special attributes like individual paintings which are greatly helpful for car retrieval have not drawn enough attention. This paper addresses these problems through multi-poses matching and re-ranking based on special attributes. Our core idea lies in query expansion method that can capture weighted attributes to build the retrieval model, which allows us to estimate invisible attributes by the visible ones to construct complete attributes vectors to car retrieval in any poses. Furthermore, we divide all attributes into two groups, special attributes and common attributes. Here special attributes represent the abnormal appearance like individual paintings or car damage while common attributes denote the intrinsic appearance of car. Using special attributes to re-rank results turns out to be beneficial to improve the retrieval performance. In the end, the experiments demonstrate the effectiveness of our approach on the car datasets.
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paper_title: A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance

paper_content:

While re-identification (Re-Id) of persons has attracted intensive attention, vehicle, which is a significant object class in urban video surveillance, is often overlooked by vision community. Most existing methods for vehicle Re-Id only achieve limited performance, as they predominantly focus on the generic appearance of vehicle while neglecting some unique identities of vehicle (e.g., license plate). In this paper, we propose a novel deep learning-based approach to PROgressive Vehicle re-ID, called “PROVID”. Our approach treats vehicle Re-Id as two specific progressive search processes: coarse-to-fine search in the feature space, and near-to-distant search in the real world surveillance environment. The first search process employs the appearance attributes of vehicle for a coarse filtering, and then exploits the Siamese Neural Network for license plate verification to accurately identify vehicles. The near-to-distant search process retrieves vehicles in a manner like human beings, by searching from near to faraway cameras and from close to distant time. Moreover, to facilitate progressive vehicle Re-Id research, we collect to-date the largest dataset named VeRi-776 from large-scale urban surveillance videos, which contains not only massive vehicles with diverse attributes and high recurrence rate, but also sufficient license plates and spatiotemporal labels. A comprehensive evaluation on the VeRi-776 shows that our approach outperforms the state-of-the-art methods by 9.28 % improvements in term of mAP.
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paper_title: Vehicle matching and recognition under large variations of pose and illumination

paper_content:

Matching vehicles subject to both large pose transformations and extreme illumination variations remains a technically challenging problem in computer vision. In this paper, we develop a new and robust framework toward matching and recognizing vehicles with both highly varying poses and drastically changing illumination conditions. By effectively estimating both pose and illumination conditions, we can re-render vehicles in the reference image to generate the relit image with the same pose and illumination conditions as the target image. We compare the relit image and the re-rendered target image to match vehicles in the original reference image and target image. Furthermore, no training is needed in our framework and re-rendered vehicle images in any other viewpoints and illumination conditions can be obtained from just one single input image. Experimental results demonstrate the robustness and efficacy of our framework, with a potential to generalize our current method from vehicles to handle other types of objects.
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paper_title: Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification

paper_content:

In this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation invariant feature embedding, local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined. With spatial-temporal regularization, the log-normal distribution is adopted to model the spatial-temporal constraints and the retrieval results can be refined. Experiments are conducted on public vehicle ReID datasets and our proposed method achieves state-of-the-art performance. Investigations of the proposed framework is conducted, including the landmark regressor and comparisons with attention mechanism. Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements.
---
paper_title: Exploiting Multi-grain Ranking Constraints for Precisely Searching Visually-similar Vehicles

paper_content:

Precise search of visually-similar vehicles poses a great challenge in computer vision, which needs to find exactly the same vehicle among a massive vehicles with visually similar appearances for a given query image. In this paper, we model the relationship of vehicle images as multiple grains. Following this, we propose two approaches to alleviate the precise vehicle search problem by exploiting multi-grain ranking constraints. One is Generalized Pairwise Ranking, which generalizes the conventional pairwise from considering only binary similar/dissimilar relations to multiple relations. The other is Multi-Grain based List Ranking, which introduces permutation probability to score a permutation of a multi-grain list, and further optimizes the ranking by the likelihood loss function. We implement the two approaches with multi-attribute classification in a multi-task deep learning framework. To further facilitate the research on precise vehicle search, we also contribute two high-quality and well-annotated vehicle datasets, named VD1 and VD2, which are collected from two different cities with diverse annotated attributes. As two of the largest publicly available precise vehicle search datasets, they contain 1,097,649 and 807,260 vehicle images respectively. Experimental results show that our approaches achieve the state-of-the-art performance on both datasets.
---
paper_title: Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals

paper_content:

Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation. It gains increasing attention because of the recent advances of person re-identification techniques. However, unlike person re-identification, the visual differences between pairs of vehicle images are usually subtle and even challenging for humans to distinguish. Incorporating additional spatio-temporal information is vital for solving the challenging re-identification task. Existing vehicle re-identification methods ignored or used over-simplified models for the spatio-temporal relations between vehicle images. In this paper, we propose a two-stage framework that incorporates complex spatio-temporal information for effectively regularizing the re-identification results. Given a pair of vehicle images with their spatio-temporal information, a candidate visual-spatio-temporal path is first generated by a chain MRF model with a deeply learned potential function, where each visual-spatio-temporal state corresponds to an actual vehicle image with its spatio-temporal information. A Siamese-CNN+Path-LSTM model takes the candidate path as well as the pairwise queries to generate their similarity score. Extensive experiments and analysis show the effectiveness of our proposed method and individual components.
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paper_title: Deep Relative Distance Learning: Tell the Difference between Similar Vehicles

paper_content:

The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from a large-scale image or video database. However, compared with person re-identification or face recognition, vehicle search problem has long been neglected by researchers in vision community. This paper focuses on an interesting but challenging problem, vehicle re-identification (a.k.a precise vehicle search). We propose a Deep Relative Distance Learning (DRDL) method which exploits a two-branch deep convolutional network to project raw vehicle images into an Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. To further facilitate the future research on this problem, we also present a carefully-organized largescale image database "VehicleID", which includes multiple images of the same vehicle captured by different realworld cameras in a city. We evaluate our DRDL method on our VehicleID dataset and another recently-released vehicle model classification dataset "CompCars" in three sets of experiments: vehicle re-identification, vehicle model verification and vehicle retrieval. Experimental results show that our method can achieve promising results and outperforms several state-of-the-art approaches.
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paper_title: Vehicle Re-identification for Automatic Video Traffic Surveillance

paper_content:

This paper proposes an approach to the vehicle reidentification problem in a multiple camera system. We focused on the re-identification itself assuming that the vehicle detection problem is already solved including extraction of a full-fledged 3D bounding box. The re-identification problem is solved by using color histograms and histograms of oriented gradients by a linear regressor. The features are used in separate models in order to get the best results in the shortest CPU computation time. The proposed method works with a high accuracy (60% true positives retrieved with 10% false positive rate on a challenging subset of the test data) in 85 milliseconds of the CPU (Core i7) computation time per one vehicle re-identification assuming the fullHD resolution video input. The applications of this work include finding important parameters such as travel time, traffic flow, or traffic information in a distributed traffic surveillance and monitoring system.
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paper_title: Vehicle identification between non-overlapping cameras without direct feature matching

paper_content:

We propose a novel method for identifying road vehicles between two nonoverlapping cameras. The problem is formulated as a same-different classification problem: probability of two vehicle images from two distinct cameras being from the same vehicle or from different vehicles. The key idea is to compute the probability without matching the two vehicle images directly, which is a process vulnerable to drastic appearance and aspect changes. We represent each vehicle image as an embedding amongst representative exemplars of vehicles within the same camera. The embedding is computed as a vector each of whose components is a nonmetric distance for a vehicle to an exemplar. The nonmetric distances are computed using robust matching of oriented edge images. A set of truthed training examples of same-different vehicle pairings across the two cameras is used to learn a classifier that encodes the probability distributions. A pair of the embeddings representing two vehicles across two cameras is then used to compute the same-different probability. In order for the vehicle exemplars to be representative for both cameras, we also propose a method for jointly selection of corresponding exemplars using the training data. Experiments on observations of over 400 vehicles under drastically illumination and camera conditions demonstrate promising results.
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paper_title: Multi-modal metric learning for vehicle re-identification in traffic surveillance environment

paper_content:

Vehicle re-identification (Re-Id) aims to retrieve the same vehicle captured by disjoint cameras at different time instants from different locations, and is a challenging task mainly due to the high similarity among the captured vehicle images in surveillance environment. With the rapid development of Convolutional Neural Network (CNN), learning-based deep features have been adopted to combine with hand-crafted features to re-identify vehicles in traffic surveillance environment. However, the two kinds of features are in different feature space, and if they are fused directly together, their complementary correlation is not able to be fully explored. To address such an issue, this paper proposes a multi-modal metric learning architecture to fuse deep features and hand-crafted ones in an end-to-end optimization network, which achieves a more robust and discriminative feature representation for vehicle re-identification. The extensive experiments on a large-scale traffic surveillance vehicle dataset demonstrate that our proposed approach substantially outperforms the state-of-the-art methods on vehicle Re-Id.
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paper_title: Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos

paper_content:

We present a novel approach for visual detection and attribute-based search of vehicles in crowded surveillance scenes. Large-scale processing is addressed along two dimensions: 1) large-scale indexing, where hundreds of billions of events need to be archived per month to enable effective search and 2) learning vehicle detectors with large-scale feature selection, using a feature pool containing millions of feature descriptors. Our method for vehicle detection also explicitly models occlusions and multiple vehicle types (e.g., buses, trucks, SUVs, cars), while requiring very few manual labeling. It runs quite efficiently at an average of 66 Hz on a conventional laptop computer. Once a vehicle is detected and tracked over the video, fine-grained attributes are extracted and ingested into a database to allow future search queries such as “Show me all blue trucks larger than 7 ft. length traveling at high speed northbound last Saturday, from 2 pm to 5 pm”. We perform a comprehensive quantitative analysis to validate our approach, showing its usefulness in realistic urban surveillance settings.
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paper_title: Matching vehicles under large pose transformations using approximate 3D models and piecewise MRF model

paper_content:

We propose a robust object recognition method based on approximate 3D models that can effectively match objects under large viewpoint changes and partial occlusion. The specific problem we solve is: given two views of an object, determine if the views are for the same or different object. Our domain of interest is vehicles, but the approach can be generalized to other man-made rigid objects. A key contribution of our approach is the use of approximate models with locally and globally constrained rendering to determine matching objects. We utilize a compact set of 3D models to provide geometry constraints and transfer appearance features for object matching across disparate viewpoints. The closest model from the set, together with its poses with respect to the data, is used to render an object both at pixel (local) level and region/part (global) level. Especially, symmetry and semantic part ownership are used to extrapolate appearance information. A piecewise Markov Random Field (MRF) model is employed to combine observations obtained from local pixel and global region level. Belief Propagation (BP) with reduced memory requirement is employed to solve the MRF model effectively. No training is required, and a realistic object image in a disparate viewpoint can be obtained from as few as just one image. Experimental results on vehicle data from multiple sensor platforms demonstrate the efficacy of our method.
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paper_title: Approximate license plate string matching for vehicle re-identification

paper_content:

From the empirical studies, it is quite difficult for the license plate recognition to perform 100% accuracy in a real world environment. Nevertheless, it is common that only a few characters are misread from a license plate recognition system. In this paper, license plate matching is used for vehicle re-identification. We evaluate several approximate string matching techniques to determine an applicable method for the domain of license plate matching. We also propose a weighted approximate string matching to further improve the performance. Our experimental results show that the generalized edit distance (GED) yields highest precision while the 2-Grams yields the highest recall. By weighting the distance cost from those two methods, our proposed weighted method gives the best performance.
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paper_title: Deep joint discriminative learning for vehicle re-identification and retrieval

paper_content:

In this paper, we propose a novel vehicle re-identification method based on a Deep Joint Discriminative Learning (DJDL) model, which utilizes a deep convolutional network to effectively extract discriminative representations for vehicle images. To exploit properties and relationship among samples in different views, we design a unified framework to combine several different tasks efficiently, including identification, attribute recognition, verification and triplet tasks. The whole network is optimized jointly via a specific batch composition design. Extensive experiments are conducted on a large-scale VehicleID [1] dataset. Experimental results demonstrate the effectiveness of our method and show that it achieves the state-of-the-art performance on both vehicle re-identification and retrieval.
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paper_title: Large-scale vehicle re-identification in urban surveillance videos

paper_content:

Vehicle, as a significant object class in urban surveillance, attracts massive focuses in computer vision field, such as detection, tracking, and classification. Among them, vehicle re-identification (Re-Id) is an important yet frontier topic, which not only faces the challenges of enormous intra-class and subtle inter-class differences of vehicles in multicameras, but also suffers from the complicated environments in urban surveillance scenarios. Besides, the existing vehicle related datasets all neglect the requirements of vehicle Re-Id: 1) massive vehicles captured in real-world traffic environment; and 2) applicable recurrence rate to give cross-camera vehicle search for vehicle Re-Id. To facilitate vehicle Re-Id research, we propose a large-scale benchmark dataset for vehicle Re-Id in the real-world urban surveillance scenario, named “VeRi”. It contains over 40,000 bounding boxes of 619 vehicles captured by 20 cameras in unconstrained traffic scene. Moreover, each vehicle is captured by 2∼18 cameras in different viewpoints, illuminations, and resolutions to provide high recurrence rate for vehicle Re-Id. Finally, we evaluate six competitive vehicle Re-Id methods on VeRi and propose a baseline which combines the color, texture, and highlevel semantic information extracted by deep neural network.
---
paper_title: Vehicle Re-Identification by Deep Hidden Multi-View Inference

paper_content:

Vehicle re-identification (re-ID) is an area that has received far less attention in the computer vision community than the prevalent person re-ID. Possible reasons for this slow progress are the lack of appropriate research data and the special 3D structure of a vehicle. Previous works have generally focused on some specific views (e.g., front); but, these methods are less effective in realistic scenarios, where vehicles usually appear in arbitrary views to cameras. In this paper, we focus on the uncertainty of vehicle viewpoint in re-ID, proposing two end-to-end deep architectures: the Spatially Concatenated ConvNet and convolutional neural network (CNN)-LSTM bi-directional loop. Our models exploit the great advantages of the CNN and long short-term memory (LSTM) to learn transformations across different viewpoints of vehicles. Thus, a multi-view vehicle representation containing all viewpoints' information can be inferred from the only one input view, and then used for learning to measure distance. To verify our models, we also introduce a Toy Car RE-ID data set with images from multiple viewpoints of 200 vehicles. We evaluate our proposed methods on the Toy Car RE-ID data set and the public Multi-View Car, VehicleID, and VeRi data sets. Experimental results illustrate that our models achieve consistent improvements over the state-of-the-art vehicle re-ID approaches.
---
paper_title: Improving triplet-wise training of convolutional neural network for vehicle re-identification

paper_content:

Vehicle re-identification (re-id) plays an important role in the automatic analysis of the drastically increasing urban surveillance videos. Similar to the other image retrieval problems, vehicle re-id suffers from the difficulties caused by various poses of vehicles, diversified illuminations, and complicated environments. Triplet-wise training of convolutional neural network (CNN) has been studied to address these challenges, where the CNN is adopted to automate the feature extraction from images, and the training adopts triplets of (query, positive example, negative example) to capture the relative similarity between them to learn representative features. The traditional triplet-wise training is weakly constrained and thus fails to achieve satisfactory results. We propose to improve the triplet-wise training at two aspects: first, a stronger constraint namely classification-oriented loss is augmented with the original triplet loss; second, a new triplet sampling method based on pairwise images is designed. Our experimental results demonstrate the effectiveness of the proposed methods that achieve superior performance than the state-of-the-arts on two vehicle re-id datasets, which are derived from real-world urban surveillance videos.
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paper_title: PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance

paper_content:

Compared with person reidentification, which has attracted concentrated attention, vehicle reidentification is an important yet frontier problem in video surveillance and has been neglected by the multimedia and vision communities. Since most existing approaches mainly consider the general vehicle appearance for reidentification while overlooking the distinct vehicle identifier, such as the license plate number, they attain suboptimal performance. In this paper, we propose PROVID, a PROgressive Vehicle re-IDentification framework based on deep neural networks. In particular, our framework not only utilizes the multimodality data in large-scale video surveillance, such as visual features, license plates, camera locations, and contextual information, but also considers vehicle reidentification in two progressive procedures: coarse-to-fine search in the feature domain, and near-to-distant search in the physical space. Furthermore, to evaluate our progressive search framework and facilitate related research, we construct the VeRi dataset, which is the most comprehensive dataset from real-world surveillance videos. It not only provides large numbers of vehicles with varied labels and sufficient cross-camera recurrences but also contains license plate numbers and contextual information. Extensive experiments on the VeRi dataset demonstrate both the accuracy and efficiency of our progressive vehicle reidentification framework.
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<format>
Title: A Survey of Advances in Vision-Based Vehicle Re-Identification

Section 1: Introduction
Description 1: Provide an overview of the importance of video surveillance in modern transportation systems, the challenges associated with vehicle re-identification (V-reID), and the motivation behind the survey.

Section 2: Vehicle Re-identification Methods
Description 2: Discuss sensor-based and vision-based methods for vehicle re-identification. Include distinctions between various categories within these methods.

Section 3: Sensor Based Methods
Description 3: Detail different types of sensor-based methods including magnetic sensors, inductive loop detectors, GPS-RFID-Cellular phones, multi-sensor, and hybrid methods, along with their strengths and weaknesses.

Section 4: Vision Based Methods
Description 4: Explore vision-based methods focusing on computer vision techniques. Differentiate between hand-crafted feature based methods and deep feature based methods.

Section 5: Datasets
Description 5: Provide detailed descriptions of various datasets used for V-reID, including CompCars, VehicleID, VeRi-776, and others. Discuss their unique characteristics and what makes them useful for V-reID research.

Section 6: Experiments and Evaluation
Description 6: Present the experimental analysis and evaluation of different V-reID methods on various benchmark datasets. Discuss the performance metrics used and summarize the findings.

Section 7: Challenges and Future Trends of V-reID
Description 7: Discuss the general challenges faced by V-reID methods, such as inter-class variations, generalization capability, long-term re-identification, and others. Highlight future research directions.

Section 8: Conclusions
Description 8: Provide a summary of the key findings from the survey, the effectiveness of different methods, and potential areas for future research in vehicle re-identification.
</format>
