Moving Objects Detection and Segmentation In Dynamic Video Backgrounds

Zhang, Jiaming; Chen, Chi Hau

doi:10.1109/ths.2007.370021

Cited by 31 publications

(20 citation statements)

References 14 publications

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“…Region-based motion detection: Zhang and Chen [64] apply a SVM classifier for block-based motion detection. Each video frame is divided into 8x8 blocks, and then classified as background or foreground.…”

Section: Enhancing the Det Ection With Another Segmentation Methodsmentioning

confidence: 99%

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Bouwmans¹,

Baf²,

Vachon³

2010

CSENG

162

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Abstract:Mixture of Gaussians is a widely used approach for background modeling to detect moving objects from static cameras. Numerous improvements of the original method developed by Stauffer and Grimson [1] have been proposed over the recent years and the purpose of this paper is to provide a survey and an original classification of these improvements. We also discuss relevant issues to reduce the computation time. Firstly, the original MOG are reminded and discussed following the challenges met in video sequences. Then, we categorize the different improvements found in the literature. We have classified them in term of strategies used to improve the original MOG and we have discussed them in term of the critical situations they claim to handle. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.

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Section: Enhancing the Det Ection With Another Segmentation Methodsmentioning

confidence: 99%

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Bouwmans¹,

Baf²,

Vachon³

2010

CSENG

162

View full text Add to dashboard Cite

show abstract

“…We can remark that to enhance the results obtained with the original MOG used in [64,103,145] can be replaced by one of the intrinsic models improvements see in the Section 2.…”

Section: Enhancing the Det Ection With Another Segmentation Methodsmentioning

confidence: 99%

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Bouwmans¹,

Baf²,

Vachon

2008

CSENG

359

127

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“…In [8]The main aim of the work is to develop efficient algorithms for the automatic segmentation of objects in image sequences. Rapid, automatic and robust segmentation is required in many aspects of multimedia applications because of its ability to automatically detect the appearance of objects and, in addition to be be used for object tracking system.The extraction of a semantically significant video object for monitoring and surveillance application is carried out in [9] Vision-based systems for remote surveillance usually involve change detection algorithms for intruders, obstacles or irregularity detection.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Traffic Detection using Horizontal and Vertical Scanning

Chawla¹,

Moghe²

2017

IJARCSSE

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Traffic congestion on city road networks is one of the main issues to be addressed by today's traffic management schemes. Traffic congestion at times leads to delay in emergency services (i.e. Ambulance, Firefighter, Police, etc.) and most of the time causes inconvenience to commuters. In this paper a new traffic detection method is proposed which is based on the horizontal and vertical scanning of video frames to obtain accurate vehicle detection. Traffic congestion is measured in terms of traffic intensity which helps to ascertain whether the traffic at a given point is low, medium or high. The proposed method shows better result as compare to other methods in terms of accuracy.Keywords: Intelligent Traffic management, Emergency Vehicle, Morphological Operations, Horizontal and vertical scanning and video processing. I. INTRODUCTIONWith increase in population and corresponding increase in vehicular traffic leads to road congestion. Such traffic congestion leads to many major problems and challenges in the urban areas and populated cities. This causes wastage of time leading to missing opportunities, frustration and delay in reaching destination.Traffic loads depend heavily on parameters such as time, day, season, weather conditions and unpredictable situations such as accidents, special events or construction activities. If these parameters are not taken into account, the traffic control system will create delays. To solve the problem of congestion, new roads are built. The only disadvantage of making new roads on the facilities is that it makes the environment more congested. So, for that reason, it is necessary to change the system rather than make new infrastructure twice. A traffic control system can solve these problems by continuously detecting and adjusting the timing of traffic signals according to the actual traffic load such a system called as intelligent traffic control system. The advantages of building an intelligent traffic control system is to reduce congestion; operating costs; provide alternative routes for travelers, increase the capacity of the infrastructure.Most urban traffic is controlled by sensors and cameras which needs to be installed on major roads and streets. The existence of an automatic traffic detection system will assist in determining the traffic [1]. These systems extract information from the larger traffic issue and help us decide to improve traffic policy [3,4]. The goal of this current research is to develop an automatic vehicle counting system, which can process videos recorded from stationary cameras over roads e.g. CCTV cameras installed near traffic intersections / junctions and counting the number of vehicles passing a spot in a particular time for further collection of vehicle / traffic data. The paper aims to automate the traffic control system on highways and streets to determine Traffic Density , identify streets, roads in order to count the cars and monitoring of roads. Figure1 shows a sample image of urban traffic.

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Moving Objects Detection and Segmentation In Dynamic Video Backgrounds

Cited by 31 publications

References 14 publications

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

Real-Time Traffic Detection using Horizontal and Vertical Scanning

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