Background subtraction techniques: a review
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paper_title: Automatic congestion detection system for underground platforms

paper_content:

An automatic monitoring system is proposed in this paper for detecting overcrowding conditions in the platforms of underground train services. Whenever overcrowding is detected, the system will notify the station operators to take appropriate actions to prevent accidents, such as people falling off or being pushed onto the tracks. The system is designed to use existing closed circuit television (CCTV) cameras for acquiring images of the platforms. In order to focus on the passengers on the platform, background subtraction and update techniques are used. In addition, due to the high variation of brightness on the platforms, a variance filter is introduced to optimize the removal of background pixels. A multi-layer feed forward neural network was developed for classifying the levels of congestion. The system was tested with recorded video from the London Bridge station, and the testing results were shown to be accurate in identifying overcrowding conditions for the unique platform environment.
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paper_title: Detecting Moving Objects, Ghosts, and Shadows in Video Streams

paper_content:

Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. The article proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.
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paper_title: Detecting Moving Objects, Ghosts, and Shadows in Video Streams

paper_content:

Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. The article proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.
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paper_title: Background subtraction based on cooccurrence of image variations

paper_content:

This paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes involving swaying trees and fluttering flags. Most methods proposed so far adjust the permissible range of the background image variations according to the training samples of background images. Thus, the detection sensitivity decreases at those pixels having wide permissible ranges. If we can narrow the ranges by analyzing input images, the detection sensitivity can be improved. For this narrowing, we employ the property that image variations at neighboring image blocks have strong correlation, also known as "cooccurrence". This approach is essentially different from chronological background image updating or morphological postprocessing. Experimental results for real images demonstrate the effectiveness of our method.
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paper_title: A Bayesian computer vision system for modeling human interactions

paper_content:

We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task. The system deals in particularly with detecting when interactions between people occur and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to meet another, and so forth. Our system combines top-down with bottom-up information in a closed feedback loop, with both components employing a statistical Bayesian approach. We propose and compare two different state-based learning architectures, namely, HMMs and CHMMs for modeling behaviors and interactions. Finally, a synthetic "Alife-style" training system is used to develop flexible prior models for recognizing human interactions. We demonstrate the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.
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paper_title: Efficient Kernel Density Estimation Using the Fast Gauss Transform with Applications to Color Modeling and Tracking

paper_content:

Many vision algorithms depend on the estimation of a probability density function from observations. Kernel density estimation techniques are quite general and powerful methods for this problem, but have a significant disadvantage in that they are computationally intensive. In this paper, we explore the use of kernel density estimation with the fast Gauss transform (FGT) for problems in vision. The FGT allows the summation of a mixture of ill Gaussians at N evaluation points in O(M+N) time, as opposed to O(MN) time for a naive evaluation and can be used to considerably speed up kernel density estimation. We present applications of the technique to problems from image segmentation and tracking and show that the algorithm allows application of advanced statistical techniques to solve practical vision problems in real-time with today's computers.
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paper_title: Background subtraction based on cooccurrence of image variations

paper_content:

This paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes involving swaying trees and fluttering flags. Most methods proposed so far adjust the permissible range of the background image variations according to the training samples of background images. Thus, the detection sensitivity decreases at those pixels having wide permissible ranges. If we can narrow the ranges by analyzing input images, the detection sensitivity can be improved. For this narrowing, we employ the property that image variations at neighboring image blocks have strong correlation, also known as "cooccurrence". This approach is essentially different from chronological background image updating or morphological postprocessing. Experimental results for real images demonstrate the effectiveness of our method.
---
paper_title: A Bayesian computer vision system for modeling human interactions

paper_content:

We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task. The system deals in particularly with detecting when interactions between people occur and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to meet another, and so forth. Our system combines top-down with bottom-up information in a closed feedback loop, with both components employing a statistical Bayesian approach. We propose and compare two different state-based learning architectures, namely, HMMs and CHMMs for modeling behaviors and interactions. Finally, a synthetic "Alife-style" training system is used to develop flexible prior models for recognizing human interactions. We demonstrate the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.
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Title: Background Subtraction Techniques: A Review

Section 1: Introduction
Description 1: Introduce the concept of background subtraction, its importance, and an overview of the various techniques used. Discuss the structure of the paper.

Section 2: The reviewed approaches: from simple to complex
Description 2: Introduce the different methods reviewed in the paper, starting from the simplest to the most complex, and provide brief descriptions of each technique.

Section 3: Running Gaussian average
Description 3: Discuss the Running Gaussian average method in detail, explaining its implementation and advantages.

Section 4: Temporal median filter
Description 4: Describe the Temporal median filter approach, its methodology, and its comparative performance to other methods.

Section 5: Mixture of Gaussians
Description 5: Explain the Mixture of Gaussians technique, how it handles changes in the background, and its specific use cases.

Section 6: Kernel Density Estimation
Description 6: Detail the Kernel Density Estimation method, its process for modeling the background, and the benefits it provides.

Section 7: Sequential Kernel Density approximation
Description 7: Discuss the Sequential Kernel Density approximation approach, including its computational aspects and performance metrics.

Section 8: Cooccurrence of image variations
Description 8: Describe this method, focusing on how it utilizes spatial cooccurrence to improve background modeling.

Section 9: Eigenbackgrounds
Description 9: Explain the Eigenbackground approach, its methodology, and how it addresses background subtraction.

Section 10: Performance analysis
Description 10: Present a comparative analysis of the reviewed methods based on speed, memory requirements, and accuracy.

Section 11: Conclusions
Description 11: Summarize the key points discussed in the paper, highlighting the strengths and weaknesses of each reviewed technique and providing final recommendations.