A Survey of Pedestrian Detection in Video
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paper_title: Vision-based system for pedestrian recognition using a tuned SVM classifier

paper_content:

Pedestrian recognition is one of the main advantages of the currently introduced autonomous cars. It is expected that millions of lives will be saved just by implementing this technology in real roads. We study this problem from two points of view, i.e., the recognition algorithm and the data. A trained binary classifier based on a tuned RFB-kernel SVM is used for predicting pedestrians on new scenarios. It is shown that tuning this classifier improves significantly the performance when compared with an SVM with linear kernel. The images are pre-processed using the HOG algorithm in order to get a pedestrian descriptor. The prediction is evaluated using the F 1 score instead of the accuracy, because it presents a better estimation of the model performance, and yields a better way of tuning the model. The model is validated using the cross-validation method; the averaged accuracy and F 1 score reached were 95% and 96.3% respectively. A database composed of 5,000 pedestrian and non-pedestrian images is used for training and testing the classifier. Several pedestrian images are analyzed after applying the algorithm to determine how the database should be completed in order to improve the detection in actual road scenarios.
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paper_title: A concurrent neural network approach to pedestrian detection in thermal imagery

paper_content:

The paper presents an original approach for pedestrian detection in thermal imagery using the neural network classifier called Concurrent Self-Organizing Maps (CSOM), previously introduced by first author; it represents a winner-takes-all collection of neural modules. The considered algorithm has the following main stages: (a) detection of the regions of interest (ROI); (b) feature selection using the Histogram of Oriented Gradients (HOG; (c) classification using a CSOM classifier with several neural modules for each class; (d) decision fusion of the SOM modules into the two final classes: pedestrians and non-pedestrians. For training and testing the proposed algorithm, we have used the OTCBVS-OSU Thermal Pedestrian Database provided by the Ohio State University. One obtains the False Positive Error Rate (FPER) of 1.79%, the False Negative Error Rate (FNER) of 0.49% and the Total Success Rate (TSR) of 98.48 %.
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paper_title: Smartphone-based Pedestrian Dead Reckoning as an indoor positioning system

paper_content:

Nowadays, personal positioning systems are more necessary to build many location-based services. Pedestrian Dead Reckoning (PDR), which is a pedestrian positioning technique using the accelerometer sensor to recognize pattern of steps, is an alternative method that has advantages in terms of infrastructure-independent. However, the variation of walking pattern on each individual will make some difficulties for the system to detect displacement. This is really interested authors to develop a sensor-based positioning system that applied generally to all individuals. In the test, 15 test subjects was taken with the distance of each 10m, 20m and 30m. Experiment begins with the feasibility test of accelerometer sensor. In this work, a smartphone with average sampling rate 63.79 Hz and standard deviation of 1.293 is used to records the acceleration. Then, the acceleration data are analyzed to detect step and to estimate the travelled distance using several methods. Detection of steps are able to make an average error of 2.925%, while the most nearly correct displacement estimation is using Scarlet experimental method which is make a distance average error of 1.39metres at all the traveled distance.
---
paper_title: Indoor 3D pedestrian tracking algorithm based on PDR using smarthphone

paper_content:

In this paper, we develop the indoor navigation system based on PDR (Pedestrian Dead Reckoning) using various sensors in smartphone. Usually PDR is consisted of step detection, step length estimation and heading estimation. The issue of PDR is step length estimation and to enhance the accuracy of step length, we apply the walking status recognition algorithm using ANN (Artificial Neuron Network). The features used in ANN are extracted through sensor signals of accelerometer and gyroscope. After recognizing the walking status, it is applied to estimate the step length. And when the status is recognized as stop, even if sensor signal is generated by redundant motion or movement of pedestrian, the moved distance is not calculated additionally and distance error is not increased. We use the barometric pressure sensor to extend the positioning area to whole building. To verify the proposed indoor navigation system, we implemented the application for android and conducted the experiment. Through the results, we demonstrated the accuracy of our system.
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paper_title: Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data

paper_content:

Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject's trousers. Two classifiers were compared, knn (k nearest neighbors) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates, which are obtained by implementing models to mobile activity recognition application which currently supports two operating systems: Symbian^3 and Android. The results show that the presented method is light and, therefore, suitable for be used in real-time recognition. In addition, the recognition rates on the smartphones were encouraging, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. Also, the results show that the method presented is not an operating system dependent.
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paper_title: Real-time pedestrian detection based on A hierarchical two-stage Support Vector Machine

paper_content:

This Paper presents an SVM (Support Vector Machine) based real-time pedestrian detection scheme for next-generation automotive vision applications. To meet the requirement of real-time detection with high accuracy, we designed the proposed system consisting of 2-stage hierarchical SVMs. In the proposed system, most of the input data are classified by the 1st stage linear SVM and only the inputs between positive and negative hyper-plane of the linear SVM are transferred to the 2nd stage non-linear SVM. This hierarchical 2-stage classifier can be suited for various systems via controlling the amount of data processed by the 2nd stage classifier, which trades off the detection accuracy and the required system resources. To make the proposed 2nd stage non-linear SVM further appropriate for various systems, a hyper-plane approximation technique by sample pruning has been adopted. By reducing the number of required SVs (Support Vectors) using this technique and controlling the amount of data processed via the 2nd stage classifier, high precision non-linear SVM can be employed in the proposed real-time pedestrian detection system. Simulations using HOG (Histogram of Oriented Gradient) features and Daimler pedestrian dataset show the proposed system provides highly accurate classification results under the real-time constraint of application.
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paper_title: Gpu architecture for stationary multisensor pedestrian detection at smart intersections

paper_content:

We present a real-time multisensor architecture for combined laser scanner and infra-red video-based pedestrian detection and tracking used within a road side unit for intersection assistance. In order to achieve outmost classification performance we propose a cascaded classifier using laser scanner hypothesis generation and histogram of oriented gradients (HOG) descriptors for video-based classification together with linear and Gaussian kernel support vector machines (SVM). The entire classification cascade is implemented on a graphics processing unit (GPU). Giving real-time performance top priority, we present novel compute unified device architecture (CUDA) implementations of a selective HOG-based feature extraction and background subtraction based on mixture of Gaussians (MOG). The classification cascade is managed by a multi-core CPU that further performs pedestrian tracking using a linear Kalman filter. Evaluation on an infra-red benchmark database and an experimental study on a real-world intersection used within the Ko-PER project confirm excellent classification and real-time performance around the clock without external illumination.
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paper_title: Integrating crowd simulation for pedestrian tracking in a multi-camera system

paper_content:

Multi-camera multi-target tracking is one of the most active research topics in computer vision. However, many challenges remain to achieve robust performance in real-world video networks. In this paper we extend the state-of-the-art single camera tracking method, with both detection and crowd simulation, to a multiple camera tracking approach that exploits crowd simulation and uses principal axis-based integration. The experiments are conducted on PETS 2009 data set and the performance is evaluated by multiple object tracking precision and accuracy (MOTP and MOTA) based on the position of each pedestrian on the ground plane. It is demonstrated that the information from crowd simulation can provide significant advantage for tracking multiple pedestrians through multiple cameras.
---
paper_title: A concurrent neural network approach to pedestrian detection in thermal imagery

paper_content:

The paper presents an original approach for pedestrian detection in thermal imagery using the neural network classifier called Concurrent Self-Organizing Maps (CSOM), previously introduced by first author; it represents a winner-takes-all collection of neural modules. The considered algorithm has the following main stages: (a) detection of the regions of interest (ROI); (b) feature selection using the Histogram of Oriented Gradients (HOG; (c) classification using a CSOM classifier with several neural modules for each class; (d) decision fusion of the SOM modules into the two final classes: pedestrians and non-pedestrians. For training and testing the proposed algorithm, we have used the OTCBVS-OSU Thermal Pedestrian Database provided by the Ohio State University. One obtains the False Positive Error Rate (FPER) of 1.79%, the False Negative Error Rate (FNER) of 0.49% and the Total Success Rate (TSR) of 98.48 %.
---
paper_title: Smartphone-based Pedestrian Dead Reckoning as an indoor positioning system

paper_content:

Nowadays, personal positioning systems are more necessary to build many location-based services. Pedestrian Dead Reckoning (PDR), which is a pedestrian positioning technique using the accelerometer sensor to recognize pattern of steps, is an alternative method that has advantages in terms of infrastructure-independent. However, the variation of walking pattern on each individual will make some difficulties for the system to detect displacement. This is really interested authors to develop a sensor-based positioning system that applied generally to all individuals. In the test, 15 test subjects was taken with the distance of each 10m, 20m and 30m. Experiment begins with the feasibility test of accelerometer sensor. In this work, a smartphone with average sampling rate 63.79 Hz and standard deviation of 1.293 is used to records the acceleration. Then, the acceleration data are analyzed to detect step and to estimate the travelled distance using several methods. Detection of steps are able to make an average error of 2.925%, while the most nearly correct displacement estimation is using Scarlet experimental method which is make a distance average error of 1.39metres at all the traveled distance.
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paper_title: Pedestrian detection method using a multilayer laserscanner: Application in urban environment

paper_content:

Pedestrian safety is a primary traffic issue in urban environment. This article deals with the detection of pedestrians by means of a laser sensor. This sensor, placed on the front of a vehicle collects information about distance distributed according to 4 laser planes. Like a vehicle, a pedestrian constitutes in the vehicle environment an obstacle which must be detected, located, then identified and tracked if necessary. In order to improve the robustness of pedestrian detection using a single laser sensor we propose here a detection system based on the fusion of information located in the 4 laser planes. In this paper, we propose a Parzen kernel method that allows first to isolate the ldquopedestrian objectsrdquo in each plane and then to carry out a decentralized fusion according to the 4 laser planes. Finally, to improve our pedestrian detection algorithm we use a MCMC based PF method allowing a closer observation of pedestrian random movement dynamics. Many experimental results validate and show the relevance of our pedestrian detection algorithm in regard to a method using only a single-row laser-range scanner.
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paper_title: Pedestrian detection at 100 frames per second

paper_content:

We present a new pedestrian detector that improves both in speed and quality over state-of-the-art. By efficiently handling different scales and transferring computation from test time to training time, detection speed is improved. When processing monocular images, our system provides high quality detections at 50 fps. We also propose a new method for exploiting geometric context extracted from stereo images. On a single CPU+GPU desktop machine, we reach 135 fps, when processing street scenes, from rectified input to detections output.
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paper_title: Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection

paper_content:

The most standard image object detectors are usually comprised of one or multiple feature extractors or classifiers within a sliding window framework. Nevertheless, this type of approach has demonstrated a very limited performance under datasets of cluttered scenes and real life situations. To tackle these issues, LIDAR space is exploited here in order to detect 2D objects in 3D space, avoiding all the inherent problems of regular sliding window techniques. Additionally, we propose a relational parts-based pedestrian detection in a probabilistic non-iid framework. With the proposed framework, we have achieved state-of-the-art performance in a pedestrian dataset gathered in a challenging urban scenario. The proposed system demonstrated superior performance in comparison with pure sliding-window-based image detectors.
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paper_title: Padati: A Robust Pedestrian Dead Reckoning System on Smartphones

paper_content:

Location based services are proving to be the next driving factors for growth in smartphones. While GPS solves the problem of accurate localization in outdoor environments, indoor localization is still an area of active research. Emergence of new generation smartphones with low cost sensors, have provided an effective way of indoor localization by pedestrian dead reckoning (PDR). We propose a robust mechanism for detecting the step of a person and estimating his step length. Our system is independent of the location and orientation of the device. Our system is shown to perform 45% better than the traditional PDR systems proposed in prior-art. Another important problem in PDR system is determining the orientation of the mobile device and the direction of user motion. Many systems assume the device to be oriented in the direction of the user motion. Some of the recent systems use accelerometer, magnetometer patterns and PCA to detect the direction of user orientation. We propose a system which uses map matching and particle filtering to determine the direction of user motion. We tabulate our findings on the feasibility of such a system.
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paper_title: Pedestrian detection using stereo night vision

paper_content:

A method is presented for pedestrian detection using a stereo night-vision system installed on the vehicle. Much of the work in this area makes use of shape information. The proposed method detects moving objects whose motions are not consistent with the movement of the background and is considered to be complementary to shape-based approaches. In this paper, two new techniques are introduced for this task for night vision, namely a two-stage method for stereo correspondence and motion detection without explicit ego-motion calculation. These techniques make use of characteristics of night-vision video data, in which humans appear as hotspots. This method works well in cases where the camera motion has a dominant translational motion with a small amount of rotational motion, which is suitable for the camera on the vehicle. Error analysis as well as experimental results are presented to validate our approach and comparisons have been carried out between our approach and frame-by-frame based pattern-recognition approaches.
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paper_title: Non-background HOG for pedestrian video detection

paper_content:

Histogram of Oriented Gradient (HOG) features are proved to be very effective for pedestrian detection in static image. However, most of the background information is wasted when the features are used to detect human in video. Especially in complex environment, the non-eliminated background gradient will affect the detection results. To improve the overall detection performance, a new feature named Non-background HOG is proposed which created a cell map using GMM for the procedure of image gradient calculation in HOG algorithm. This new algorithm not only is capable of reducing the influence of background gradient, but also speeds up the extraction running time. Evaluation experiment demonstrated that the non-background HOG algorithm gives a better performance than classic HOG in pedestrian video detection.
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paper_title: Fast Pedestrian Detection with Laser and Image Data Fusion

paper_content:

In this paper, we proposed a pedestrian detection system based on laser and image data fusion. The high speed of laser data based location and precise of image based classification are fully explored. First, laser scanner point data is clustered into segments, each of which implies a pedestrian candidate. Then, the segments are projected to the image domain to form regions of interest (ROI) on the image, given camera calibration parameters. Finally two SVM classifiers on Histogram of Oriented Gradient (HOG) features are used to precisely locate pedestrians on the ROI. Experiments report over 30 times higher speed than the state-of-the-art method and a comparable detection rate.
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paper_title: Tracking pedestrians with incremental learned intensity and contour templates for PTZ camera visual surveillance

paper_content:

This paper presents a novel particle-based pedestrian tracking algorithm for PTZ visual surveillance. Most of the state-of-art particle-based tracking algorithms are challenged due to lacking of a reliable moving object detection and drastic scale along with perspective shift of the target. Therefore, pure intensity based algorithms usually miss the target gradually without other features for correcting target location. Our method learns and maintains a contour template of the target besides intensity. Taking into account both the evolution and sudden change of the pedestrian contour, the proposed tracking algorithm maintains several sets of profiles from different perspectives and evolves them incrementally. The effectiveness of our tracking algorithm with extra contour measurement is tested over several surveillance records captured from PTZ camera and estimates the location more robustly than other cutting edge tracking algorithms compared in our experiments.
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paper_title: An In-vehicle System for Pedestrian Detection

paper_content:

Pedestrian detection is a significant task in driver assistance systems. This paper presents an in-vehicle system for pedestrian detection. To speed up computation, we parallel implemented the state of the art detection algorithm on a NVIDIA GPU. The experiments demonstrate that our implementation can achieve accurate and real time detection.
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paper_title: Human detection and tracking using apparent features under multi-cameras with non-overlapping

paper_content:

This paper describes a human detection and tracking system under multi-cameras with non-overlapping views using apparent features only. Our system is able to first detect people and then perform object matching. In the distributed intelligent surveillance system, computers need to detect pedestrians automatically under multi-cameras probably with non-overlapping views for providing a steady and continuous tracking of the pedestrian targets. In this paper, we combine Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) to detect human and segment human body from the background using GrabCut algorithm. We also study the method of pedestrian feature extraction and object matching based on appearance. We connect all the modules above in series to obtain a complete system and test it on samples we collect over three cameras with non-overlapping views to prove the effectiveness. We believe that our system will be helpful to the development of the public security system.
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paper_title: Real-time detection and tracking of pedestrians at intersections using a network of laserscanners

paper_content:

Accident analysis shows that the majority of accidents with body injuries occur in urban areas and more than 50 percent of those urban accidents happen at intersections. Due to that a major aim of the Ko-PER project, which is part of research initiative Ko-FAS, is to improve safety at intersections by infrastructure based perception. To recognize and track the moving objects, a network of laserscanner sensors observes the intersection and provides a 3D profile of the current scene. By means of the 3D measurements a robust and adaptive Gaussian mixture background model is trained to segment the measurements of dynamic objects and static objects. After the segmentation, the foreground points of each sensor are clustered based on the density of the point clouds and finally pedestrians are classified using dimension features. This paper focuses on tracking of pedestrians, which are the most vulnerable road users. In order to be able to integrate dependencies between the states of the pedestrians, a random finite set particle filter is used to track the pedestrians. The performance of the laserscanner based tracking system is shown and evaluated with measurements from the Ko-PER test intersection at Conti-Safety-Park. Therefore, the optimal subpattern assignment (OSPA) metric is used to evaluate the object recognition and tracking system.
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paper_title: Pedestrian Detection: An Evaluation of the State of the Art

paper_content:

Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches to detecting pedestrians in monocular images has grown steadily. However, multiple data sets and widely varying evaluation protocols are used, making direct comparisons difficult. To address these shortcomings, we perform an extensive evaluation of the state of the art in a unified framework. We make three primary contributions: 1) We put together a large, well-annotated, and realistic monocular pedestrian detection data set and study the statistics of the size, position, and occlusion patterns of pedestrians in urban scenes, 2) we propose a refined per-frame evaluation methodology that allows us to carry out probing and informative comparisons, including measuring performance in relation to scale and occlusion, and 3) we evaluate the performance of sixteen pretrained state-of-the-art detectors across six data sets. Our study allows us to assess the state of the art and provides a framework for gauging future efforts. Our experiments show that despite significant progress, performance still has much room for improvement. In particular, detection is disappointing at low resolutions and for partially occluded pedestrians.
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paper_title: Action dataset — A survey

paper_content:

Human action understanding and recognition have various demands for different applications in the field of computer vision and human-machine interaction. Due to these issues, more than a decade, extensive researches are going on in this arena — to recognize various actions and activities. Researchers have been exploiting various action datasets and some of them become prominent. Though there are some good datasets, unfortunately, to have a strong survey on these datasets has been a long due. This paper attempts this and presents the key datasets and analyzes them in different perspectives.
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paper_title: Fast Feature Pyramids for Object Detection

paper_content:

Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detection algorithms that are as accurate, and considerably faster, than the state-of-the-art. The computational bottleneck of many modern detectors is the computation of features at every scale of a finely-sampled image pyramid. Our key insight is that one may compute finely sampled feature pyramids at a fraction of the cost, without sacrificing performance: for a broad family of features we find that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid. Extrapolation is inexpensive as compared to direct feature computation. As a result, our approximation yields considerable speedups with negligible loss in detection accuracy. We modify three diverse visual recognition systems to use fast feature pyramids and show results on both pedestrian detection (measured on the Caltech, INRIA, TUD-Brussels and ETH data sets) and general object detection (measured on the PASCAL VOC). The approach is general and is widely applicable to vision algorithms requiring fine-grained multi-scale analysis. Our approximation is valid for images with broad spectra (most natural images) and fails for images with narrow band-pass spectra (e.g., periodic textures).
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paper_title: Real-time pedestrian detection with deformable part models

paper_content:

We describe a real-time pedestrian detection system intended for use in automotive applications. Our system demonstrates superior detection performance when compared to many state-of-the-art detectors and is able to run at a speed of 14 fps on an Intel Core i7 computer when applied to 640×480 images. Our approach uses an analysis of geometric constraints to efficiently search feature pyramids and increases detection accuracy by using a multiresolution representation of a pedestrian model to detect small pixel-sized pedestrians normally missed by a single representation approach. We have evaluated our system on the Caltech Pedestrian benchmark which is currently the largest publicly available pedestrian dataset at the time of this publication. Our system shows a detection rate of 61% with 1 false positive per image (FPPI) whereas recent other state-of-the-art detectors show a detection rate of 50% ∼ 61% under the ‘reasonable’ test scenario (explained later). Furthermore, we also demonstrate the practicality of our system by conducting a series of use case experiments on selected videos of Caltech dataset.
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paper_title: Human detection and segmentation in the crowd environment by coimbining APD with HLBD approaches

paper_content:

The video-surveillance systems are popularly used in crowd monitoring and people detection in that crowd is estimated for security of humans in public places and also managing the resources. Humans can be detected and segmented through videos and this is very much important for security purposes.Today, there are many challenging problems in video surveillance due to variations in poses, clothing, lighting, illumination, occlusion and complexity of the background. The challenges in a complex background for video surveillance can be prevented in the human detection by using a model based approach.The template based shape matching algorithm is used to matching the different shapes of human.This approach is used to matching the boundary of the human shapes with detected image. Segmentation of the humans in a crowd scene takes both advantages of local region based detector and global shape boundary detector methods.Some of part based pose detector failed to detect different poses in the crowd environment.The proposed approach, different poses of human can be extracted by using adaptive pose detector.The different poses can be described by high level boundary descriptors.Normalized histograms are used as feature descriptors to find the optimal poses.By combining both APD and HLBD approaches, the misdetections of human are prevented in crowd environment. To classify human or non-human pattern in a crowd image sequence by using multiclass-SVM classifier. The proposed method is validated and evaluated using two public pedestrian dataset such as INRIA and crowded sequences, CAVIAR Benchmark and Munich Airport Datasets.
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paper_title: Blob Motion Statistics for Pedestrian Detection

paper_content:

Video analysis aiming at efficient pedestrian detection is an important research area in computer vision and robotics. Although this is a well studied topic, successful detection still remains a challenge in outdoor, low resolution images. We present efficient detection metrics which consider the fact that human movement presents some characteristic patterns. Unlike many methods which perform an intra-blob analysis based on motion masks, we approach the problem without necessarily considering the pixel distribution inside the blob. Therefore, we apply periodicity analysis not to the pixel luminances inside the blob, but by analyzing the motion statistics of the tracked blob as a whole. We propose the use of three cues: (i) a cyclic behavior in the blob trajectory, (ii) an in-phase relationship between the change in blob size and position, and (iii) a correlation between blob size and vertical position, assuming that the camera is set up sufficiently high. These features are combined according to the Bayes classifier for improved performance. Experiments present numerical error rates and comparisons with other methods, illustrating the applicability of the proposed method.
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paper_title: Robust techniques for background subtraction in urban traffic video

paper_content:

Identifying moving objects from a video sequence is a fundamental and ::: critical task in many computer-vision applications. A common approach ::: is to perform background subtraction, which identifies moving objects ::: from the portion of a video frame that differs significantly from a ::: background model. There are many challenges in developing a good ::: background subtraction algorithm. First, it must be robust against ::: changes in illumination. Second, it should avoid detecting ::: non-stationary background objects such as swinging leaves, rain, snow, ::: and shadow cast by moving objects. Finally, its internal background ::: model should react quickly to changes in background such as starting ::: and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.
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paper_title: An HOG-LBP human detector with partial occlusion handling

paper_content:

By combining Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) as the feature set, we propose a novel human detection approach capable of handling partial occlusion. Two kinds of detectors, i.e., global detector for whole scanning windows and part detectors for local regions, are learned from the training data using linear SVM. For each ambiguous scanning window, we construct an occlusion likelihood map by using the response of each block of the HOG feature to the global detector. The occlusion likelihood map is then segmented by Mean-shift approach. The segmented portion of the window with a majority of negative response is inferred as an occluded region. If partial occlusion is indicated with high likelihood in a certain scanning window, part detectors are applied on the unoccluded regions to achieve the final classification on the current scanning window. With the help of the augmented HOG-LBP feature and the global-part occlusion handling method, we achieve a detection rate of 91.3% with FPPW= 10−6, 94.7% with FPPW= 10−5, and 97.9% with FPPW= 10−4 on the INRIA dataset, which, to our best knowledge, is the best human detection performance on the INRIA dataset. The global-part occlusion handling method is further validated using synthesized occlusion data constructed from the INRIA and Pascal dataset.
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paper_title: Motion-based background subtraction using adaptive kernel density estimation

paper_content:

Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of static or quasi-static structures. When the scene exhibits a persistent dynamic behavior in time, such an assumption is violated and detection performance deteriorates. In this paper, we propose a new method for the modeling and subtraction of such scenes. Towards the modeling of the dynamic characteristics, optical flow is computed and utilized as a feature in a higher dimensional space. Inherent ambiguities in the computation of features are addressed by using a data-dependent bandwidth for density estimation using kernels. Extensive experiments demonstrate the utility and performance of the proposed approach.
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paper_title: Shape-based pedestrian detection and tracking

paper_content:

This paper presents a large scale experimental study on pedestrian detection. The focus of the study is the Chamfer System, a generic system for shape-based object recognition. Matching involves a simultaneous coarse-to-fine approach over a template hierarchy and over the transformation parameters based on correlation with (chamfer) distance-transformed images. Candidate solutions are verified by a neural network with local receptive fields, using a richer set of texture features. Detection is supplemented by an alpha-beta tracker which integrates results over time; the tracker compensates for momentarily missing detections due to image noise or occlusions. For this study, an extensive database of 4762 pedestrian images was compiled with precise ground-truth data. System performance was analyzed by several ROC curves. Although not viable for real-world deployment yet, system performance is shown to be quite promising.
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paper_title: A cascade framework for unoccluded and occluded pedestrian detection

paper_content:

This paper presents a novel pedestrian detection framework for efficient detection of both unoccluded and occluded pedestrians, thereby proposing an efficient technique for pedestrian detection in real-time environment. Our framework consists of two layers of detection, the first layer using full body detectors for accurate detection of unoccluded pedestrians and then a cascaded layer of part based detectors to efficiently detect the occluded pedestrians. The full body detectors based techniques are state-of-the art for unoccluded pedestrian detection and the part based model is a viable choice for partially occluded pedestrian detection. In our part based model, we use six parts; three horizontal parts and three vertical parts thereby creating a model that is robust to varying degrees and types of occlusions. Each detection layer utilizes multiple modalities (cues) namely; intensity, dense stereo and dense flow. The use of part based detectors as the cascaded layer also increases the unoccluded pedestrian detection rate by correctly detecting the pedestrians that had been misclassified by the first layer. Thus, the second layer of part based detectors has a synergic effect on the first layer.
---
paper_title: Indoor 3D pedestrian tracking algorithm based on PDR using smarthphone

paper_content:

In this paper, we develop the indoor navigation system based on PDR (Pedestrian Dead Reckoning) using various sensors in smartphone. Usually PDR is consisted of step detection, step length estimation and heading estimation. The issue of PDR is step length estimation and to enhance the accuracy of step length, we apply the walking status recognition algorithm using ANN (Artificial Neuron Network). The features used in ANN are extracted through sensor signals of accelerometer and gyroscope. After recognizing the walking status, it is applied to estimate the step length. And when the status is recognized as stop, even if sensor signal is generated by redundant motion or movement of pedestrian, the moved distance is not calculated additionally and distance error is not increased. We use the barometric pressure sensor to extend the positioning area to whole building. To verify the proposed indoor navigation system, we implemented the application for android and conducted the experiment. Through the results, we demonstrated the accuracy of our system.
---
paper_title: Pedestrian Detection Based on ISC in Infrared Images

paper_content:

A novel approach toward pedestrian detection applied to infrared images using improved Shape Context feature (ISC) is proposed. We apply Bilateral Filtering for images to preserve the information of large sharp edges and to facilitate the task of Shape Context feature (SC) extracting. Then, we study an image descriptor based on an improved Shape Context feature (ISC), which is more robust to object deformation. For a test image, improved SC features are extracted and matched to the codebook. A voting scheme then obtains object locations from the matching results. Experimental results with Infrared Image Database demonstrate that our method is suitable for infrared images, and is promising.
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paper_title: Pedestrian detection by scene dependent classifiers with generative learning

paper_content:

Recently, pedestrian detection from in-vehicle camera images is becoming an crucial technology for Intelligent Transportation Systems (ITS). However, it is difficult to detect pedestrians accurately in various scenes by obtaining training samples. To tackle this problem, we propose a method to construct scene dependent classifiers to improve the accuracy of pedestrian detection. The proposed method selects an appropriate classifier based on the scene information that is a category of appearance associated with location information. To construct scene dependent classifiers, the proposed method introduces generative learning for synthesizing scene dependent training samples. Experimental results showed that the detection accuracy of the proposed method outperformed the comparative method, and we confirmed that scene dependent classifiers improved the accuracy of pedestrian detection.
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paper_title: Real-time pedestrian detection based on A hierarchical two-stage Support Vector Machine

paper_content:

This Paper presents an SVM (Support Vector Machine) based real-time pedestrian detection scheme for next-generation automotive vision applications. To meet the requirement of real-time detection with high accuracy, we designed the proposed system consisting of 2-stage hierarchical SVMs. In the proposed system, most of the input data are classified by the 1st stage linear SVM and only the inputs between positive and negative hyper-plane of the linear SVM are transferred to the 2nd stage non-linear SVM. This hierarchical 2-stage classifier can be suited for various systems via controlling the amount of data processed by the 2nd stage classifier, which trades off the detection accuracy and the required system resources. To make the proposed 2nd stage non-linear SVM further appropriate for various systems, a hyper-plane approximation technique by sample pruning has been adopted. By reducing the number of required SVs (Support Vectors) using this technique and controlling the amount of data processed via the 2nd stage classifier, high precision non-linear SVM can be employed in the proposed real-time pedestrian detection system. Simulations using HOG (Histogram of Oriented Gradient) features and Daimler pedestrian dataset show the proposed system provides highly accurate classification results under the real-time constraint of application.
---
paper_title: Padati: A Robust Pedestrian Dead Reckoning System on Smartphones

paper_content:

Location based services are proving to be the next driving factors for growth in smartphones. While GPS solves the problem of accurate localization in outdoor environments, indoor localization is still an area of active research. Emergence of new generation smartphones with low cost sensors, have provided an effective way of indoor localization by pedestrian dead reckoning (PDR). We propose a robust mechanism for detecting the step of a person and estimating his step length. Our system is independent of the location and orientation of the device. Our system is shown to perform 45% better than the traditional PDR systems proposed in prior-art. Another important problem in PDR system is determining the orientation of the mobile device and the direction of user motion. Many systems assume the device to be oriented in the direction of the user motion. Some of the recent systems use accelerometer, magnetometer patterns and PCA to detect the direction of user orientation. We propose a system which uses map matching and particle filtering to determine the direction of user motion. We tabulate our findings on the feasibility of such a system.
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paper_title: Histograms of oriented gradients for human detection

paper_content:

We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
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paper_title: Counting people in crowds with a real-time network of simple image sensors

paper_content:

Estimating the number of people in a crowded environment is a central task in civilian surveillance. Most vision-based counting techniques depend on detecting individuals in order to count, an unrealistic proposition in crowded settings. We propose an alternative approach that directly estimates the number of people. In our system, groups of image sensors segment foreground objects from the background, aggregate the resulting silhouettes over a network, and compute a planar projection of the scene's visual hull. We introduce a geometric algorithm that calculates bounds on the number of persons in each region of the projection, after phantom regions have been eliminated. The computational requirements scale well with the number of sensors and the number of people, and only limited amounts of data are transmitted over the network. Because of these properties, our system runs in real-time and can be deployed as an untethered wireless sensor network. We describe the major components of our system, and report preliminary experiments with our first prototype implementation.
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paper_title: Non-background HOG for pedestrian video detection

paper_content:

Histogram of Oriented Gradient (HOG) features are proved to be very effective for pedestrian detection in static image. However, most of the background information is wasted when the features are used to detect human in video. Especially in complex environment, the non-eliminated background gradient will affect the detection results. To improve the overall detection performance, a new feature named Non-background HOG is proposed which created a cell map using GMM for the procedure of image gradient calculation in HOG algorithm. This new algorithm not only is capable of reducing the influence of background gradient, but also speeds up the extraction running time. Evaluation experiment demonstrated that the non-background HOG algorithm gives a better performance than classic HOG in pedestrian video detection.
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paper_title: An iterative image registration technique with an application to stereo vision

paper_content:

Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is taster because it examines far fewer potential matches between the images than existing techniques Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show how our technique can be adapted tor use in a stereo vision system.
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paper_title: Determining Optical Flow

paper_content:

Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantified rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.
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paper_title: Shape matching and object recognition using shape contexts

paper_content:

We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.
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paper_title: Distributed Object Detection With Linear SVMs

paper_content:

In vision and learning, low computational complexity and high generalization are two important goals for video object detection. Low computational complexity here means not only fast speed but also less energy consumption. The sliding window object detection method with linear support vector machines (SVMs) is a general object detection framework. The computational cost is herein mainly paid in complex feature extraction and innerproduct-based classification. This paper first develops a distributed object detection framework (DOD) by making the best use of spatial-temporal correlation, where the process of feature extraction and classification is distributed in the current frame and several previous frames. In each framework, only subfeature vectors are extracted and the response of partial linear classifier (i.e., subdecision value) is computed. To reduce the dimension of traditional block-based histograms of oriented gradients (BHOG) feature vector, this paper proposes a cell-based HOG (CHOG) algorithm, where the features in one cell are not shared with overlapping blocks. Using CHOG as feature descriptor, we develop CHOG-DOD as an instance of DOD framework. Experimental results on detection of hand, face, and pedestrian in video show the superiority of the proposed method.
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paper_title: Part-based pedestrian detection using grammar model and ABM-HoG features

paper_content:

To handle the pedestrian appearance and pose variations in complex traffic environments, we present one part-based pedestrian detection approach using a stochastic grammar model in this paper. The And-Or graph model is introduced to represent the human body as an assembly of compositional and reconfigurable parts. Thus, the task of detection is converted into the human parsing problem, which is a Bayesian inference process. We model the appearance of pedestrian parts in a rich feature representation. This appearance model enhances the Histogram of Gradients (HoG) map with Active Basis Model (ABM), which is a sparse deformable template depicting salient structures of objects. Then, geometry constraints among parts are described by Gaussian distributions. Finally, the bottom-up parsing inference is conducted by aggregating scores to get the pedestrian detection responses. In experiments, we show the superiority of our appearance model, as well as the reliable pedestrian detection results of our approach in complex traffic scenes.
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paper_title: Pedestrians detection using a cascade of LBP and HOG classifiers

paper_content:

Accurate pedestrian detection in urban environment is a highly explored research field. We propose a new approach in pedestrian detection that combines the popular Local Binary Patterns and Histogram of Oriented Gradient features. The novelty of our work resides in the combination of a reduced HOG feature vector with uniform LBP patterns for the pedestrian data representation. Another contribution resides in the design and implementation of a two-stage cascade classifier of Support Vector Machine. Our method has been trained and tested on reference benchmark datasets and it proved to have good results.
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paper_title: Fast human detection using Gaussian Particle Swarm Optimization

paper_content:

Human detection is a challenging task in many fields because it is difficult to detect humans due to their varying appearance and posture. The evaluation speed of the method is important as well as its accuracy. In this paper, we propose a novel method using Gaussian Particle Swarm Optimization (Gaussian-PSO) for human detection with the Histograms of Oriented Gradients (HOG) feature to achieve a fast and accurate performance. Keeping the robustness of HOG feature on human detection, we raise the process speed in detection process so that it can be used for real-time applications. These advantages are given by a simple process which needs only one linear-SVM classifier with HOG features and Gaussian-PSO procedure.
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paper_title: Heading change detection for indoor navigation with a Smartphone camera

paper_content:

A comfortable and useful pedestrian navigation system is accurate, reasonably priced, easy to use, and light to carry. Smartphones are attractive platforms for the navigation systems due to their small size, low cost, and the fact that they are already carried routinely by many pedestrians. Pedestrian navigation is mostly needed in GNSS degraded and denied areas such as indoors and in urban canyons. Positioning in these environments is still a challenging task. Aiding from other location sensors is typically needed. Obtaining a reliable heading e.g. from a digital compass is one of the most challenging tasks indoors because of the environmental disturbances to the sensor measurements. Visual-aiding has been considered for solving this problem for the last few years. Most algorithms are however often too massive for a Smartphone with limited computing power and storage space. This paper presents a lightweight algorithm for solving the problem. It is based on vanishing points calculated from lines in consecutive images. Results from a field test with a Nokia Smartphone have demonstrated that the performance of the algorithm in calculating the heading change is much better than that of the built-in digital compass. The heading change can be detected with the proposed algorithm at about 1 Hz frequency under the PC environment. It has potential to be adopted for a Smartphone platform
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paper_title: Pedestrian detection in single frame by edgelet-LBP part detectors

paper_content:

This paper proposes a method for human detection in crowded scene from static images. We introduce to combine edgelet and LBP features to obtain more discriminative representations for local area. To cope with partial occlusion, part detectors are learned using real AdaBoost in bootstrap way. Responses of part detectors are combined to form the final results. We test our approach on several common datasets and compare the proposed method with others. The experimental results prove that our method is comparable to the state-of-the-art method and performs well on crowded scenes.
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paper_title: Real-time detection and tracking of pedestrians at intersections using a network of laserscanners

paper_content:

Accident analysis shows that the majority of accidents with body injuries occur in urban areas and more than 50 percent of those urban accidents happen at intersections. Due to that a major aim of the Ko-PER project, which is part of research initiative Ko-FAS, is to improve safety at intersections by infrastructure based perception. To recognize and track the moving objects, a network of laserscanner sensors observes the intersection and provides a 3D profile of the current scene. By means of the 3D measurements a robust and adaptive Gaussian mixture background model is trained to segment the measurements of dynamic objects and static objects. After the segmentation, the foreground points of each sensor are clustered based on the density of the point clouds and finally pedestrians are classified using dimension features. This paper focuses on tracking of pedestrians, which are the most vulnerable road users. In order to be able to integrate dependencies between the states of the pedestrians, a random finite set particle filter is used to track the pedestrians. The performance of the laserscanner based tracking system is shown and evaluated with measurements from the Ko-PER test intersection at Conti-Safety-Park. Therefore, the optimal subpattern assignment (OSPA) metric is used to evaluate the object recognition and tracking system.
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paper_title: Pedestrian Detection Based on Blob Motion Statistics

paper_content:

Pedestrian detection based on video analysis is a key functionality in automated surveillance systems. In this paper, we present efficient detection metrics that consider the fact that human movement presents distinctive motion patterns. Contrary to several methods that perform an intrablob analysis based on motion masks, we approach the problem without necessarily considering the periodic pixel motion inside the blob. As such, we do not analyze periodicity in the pixel luminances, but in the motion statistics of the tracked blob as a whole. For this, we propose the use of the following cues: 1) a cyclic behavior in the blob trajectory, and 2) an in-phase relationship between the change in blob size and position. In addition, we also exploit the relationship between blob size and vertical position, assuming that the camera is positioned sufficiently high. If the homography between the camera and the ground is known, the features are normalized by transforming the blob size to the real person size. For improved performance, we combine these features using the Bayes classifier. We also present a theoretical statistical analysis to evaluate the system performance in the presence of noise. We perform online experiments in a real industrial scenario and also with videos from well-known databases. The results illustrate the applicability of the proposed features.
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paper_title: People Counting across Multiple Cameras for Intelligent Video Surveillance

paper_content:

Pedestrian counting is widely used in civilian surveillance. In this paper, we present a people counting system which estimates the number of people across multiple cameras with partial overlapping Fields Of Views (FOVs). The main contributions of this paper include: 1) we propose a multi-object detection and tracking method by means of synthesizing the local-feature-level information into object-level based on an electing and weighting mechanism (EWM), 2) We present a scheme to integrate the counting results from multiple cameras. Through homograpy transform and similarity measurement rules, the system can find the objects in overlapping FOVs and finally estimate the integrated number of people across multiple cameras. Experiments results demonstrate that our system is effective and accurate for multi-camera people counting.
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paper_title: An efficient self-learning people counting system

paper_content:

People counting is a challenging task and has attracted much attention in the area of video surveillance. In this paper, we present an efficient self-learning people counting system which can count the exact number of people in a region of interest. This system based on bag-of-features model can effectively detect the pedestrians some of which are usually treated as background because they are static or move slowly. The system can also select pedestrian and non-pedestrian samples automatically and update the classifier in real-time to make it more suitable for certain specific scene. Experimental results on a practical public dataset named CASIA Pedestrian Counting Dataset show that the proposed people counting system is robust and accurate.
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paper_title: Indoor 3D pedestrian tracking algorithm based on PDR using smarthphone

paper_content:

In this paper, we develop the indoor navigation system based on PDR (Pedestrian Dead Reckoning) using various sensors in smartphone. Usually PDR is consisted of step detection, step length estimation and heading estimation. The issue of PDR is step length estimation and to enhance the accuracy of step length, we apply the walking status recognition algorithm using ANN (Artificial Neuron Network). The features used in ANN are extracted through sensor signals of accelerometer and gyroscope. After recognizing the walking status, it is applied to estimate the step length. And when the status is recognized as stop, even if sensor signal is generated by redundant motion or movement of pedestrian, the moved distance is not calculated additionally and distance error is not increased. We use the barometric pressure sensor to extend the positioning area to whole building. To verify the proposed indoor navigation system, we implemented the application for android and conducted the experiment. Through the results, we demonstrated the accuracy of our system.
---
paper_title: Counting people in crowds with a real-time network of simple image sensors

paper_content:

Estimating the number of people in a crowded environment is a central task in civilian surveillance. Most vision-based counting techniques depend on detecting individuals in order to count, an unrealistic proposition in crowded settings. We propose an alternative approach that directly estimates the number of people. In our system, groups of image sensors segment foreground objects from the background, aggregate the resulting silhouettes over a network, and compute a planar projection of the scene's visual hull. We introduce a geometric algorithm that calculates bounds on the number of persons in each region of the projection, after phantom regions have been eliminated. The computational requirements scale well with the number of sensors and the number of people, and only limited amounts of data are transmitted over the network. Because of these properties, our system runs in real-time and can be deployed as an untethered wireless sensor network. We describe the major components of our system, and report preliminary experiments with our first prototype implementation.
---
paper_title: Pedestrian detection in industrial environments: Seeing around corners

paper_content:

Situational awareness for industrial vehicles is crucial to ensure safety of personnel and equipment. While human drivers and onboard sensors are able to detect obstacles and pedestrians within line-of-sight, in complex environments initially occluded or obscured dynamic objects can unpre dictably enter the path of a vehicle. We propose a safety system which integrates a vision-based offboard pedestrian tracking subsystem with an onboard localisation and navigation subsystem. This combination enables warnings to be communicated and effectively extends the vehicle controllers field of view to include areas that would otherwise be blind spots. A simple flashing light interface in the vehicle cabin provides a clear and intuitive interface to alert drivers of potential collisions. We implemented and tested the proposed solution on an automated industrial vehicle to verify the applicability for both human drivers and under autonomous operation.
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paper_title: Real-time navigation for a personal mobility in an environment with pedestrians

paper_content:

This paper describes a navigation system in a dynamic environment for a two-wheeled inverted pendulum mobile robot, PMR. Our system is organized by localization, detection and tracking of pedestrians, and trajectory planner. The localization is robust to effects of moving obstacles and pitching movements of the robot, and the trajectory planner creates a path with a certain smoothness considering movements of pedestrians. In addition, the planner introduces strategies to avoid pedestrians to be friendly to pedestrians around the robot. Besides, our system can run on two laptop PCs in real time. Finally, we show experimental results as well.
---

<format>
Title: A Survey of Pedestrian Detection in Video

Section 1: INTRODUCTION
Description 1: This section introduces the topic of pedestrian detection in video, explains the importance of pedestrian detection, lists applications that benefit from this technology, and outlines the goals and contributions of the paper.

Section 2: DISCUSSIONS
Description 2: This section discusses each stage in the process of pedestrian detection, including various input devices, datasets, and methods used for detecting pedestrians.

Section 3: Input Devices
Description 3: This sub-section details the types of input devices used in pedestrian detection, discussing their advantages and limitations in different research scenarios.

Section 4: Datasets
Description 4: This sub-section provides a summary of the commonly used datasets for pedestrian detection, explaining their use and significance in research.

Section 5: Detection Process
Description 5: This sub-section breaks down the stages of the detection process from pre-processing to object segmentation and classification, describing various methods and techniques used in these stages.

Section 6: Pedestrian Detection Applications
Description 6: This sub-section describes the different applications of pedestrian detection technology, demonstrating how it is utilized in various fields such as robotics, surveillance, and traffic analysis.

Section 7: FUTURE RESEARCHES
Description 7: This section discusses potential areas for future research, highlighting unresolved issues and the need for further development in speed, accuracy, and optimization of pedestrian detection methods.

Section 8: CONCLUSIONS
Description 8: This section summarizes the survey findings, emphasizing the importance of the research reviewed and providing an overview of potential future directions for pedestrian detection research.
</format>
