<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The scope of this paper is a video surveillance system constituted of three principal modules, segmentation module, vehicle classification and vehicle counting. The segmentation is based on a background subtraction by using the Codebooks method. This step aims to define the regions of interest associated with vehicles. To classify vehicles in their type, our system uses the histograms of oriented gradient followed by support vector machine. Counting and tracking vehicles will be the last task to be performed. The presence of partial occlusion involves the decrease of the accuracy of vehicle segmentation and classification, which directly impacts the robustness of a video surveillance system. Therefore, a novel method to handle the partial occlusions based on vehicle classification process have developed. The results achieved have shown that the accuracy of vehicle counting and classification exceeds the accuracy measured in some existing systems.</p></abstract>

The scope of this paper is a video surveillance system constituted of three principal modules, segmentation module, vehicle classification and vehicle counting. The segmentation is based on a background subtraction by using the Codebooks method. This step aims to define the regions of interest associated with vehicles. To classify vehicles in their type, our system uses the histograms of oriented gradient followed by support vector machine. Counting and tracking vehicles will be the last task to be performed. The presence of partial occlusion involves the decrease of the accuracy of vehicle segmentation and classification, which directly impacts the robustness of a video surveillance system. Therefore, a novel method to handle the partial occlusions based on vehicle classification process have developed. The results achieved have shown that the accuracy of vehicle counting and classification exceeds the accuracy measured in some existing systems.

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The scope of this paper is a video surveillance system constituted of three principal modules, segmentation module, vehicle classification and vehicle...

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Online veröffentlicht: 22. Nov. 2017

Seitenbereich: 297 - 306

DOI: https://doi.org/10.1515/ttj-2017-0027

Schlüsselwörter
Intelligent transportation systems, Vehicle detection, Occlusion handling, Vehicle classification, Background subtraction

© Transport and Telecommunication Institute

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Online veröffentlicht: 22. Nov. 2017

Seitenbereich: 297 - 306

DOI: https://doi.org/10.1515/ttj-2017-0027

SchlüsselwörterIntelligent transportation systems, Vehicle detection, Occlusion handling, Vehicle classification, Background subtraction

© Transport and Telecommunication Institute

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Intelligent transportation systems, Vehicle detection, Occlusion handling, Vehicle classification, Background subtraction