Video analysis for traffic anomaly detection using support vector machines

Praveen Batapati; Duy Tran; Weihua Sheng; Meiqin Liu; Ruili Zeng

doi:10.1109/WCICA.2014.7053655

Video analysis for traffic anomaly detection using support vector machines

Praveen Batapati
, Duy Tran
, Weihua Sheng
, Meiqin Liu
, Ruili Zeng

Oklahoma State University
Zhejiang University
Tianjin University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

In this paper we present a video-based traffic surveillance system which analyzes the video footage and uses the trajectories of the vehicles to detect any anomalous vehicle behavior at a traffic intersection. The trajectory analysis is done using support vector machines (SVMs). We also discuss the trajectory representation and trajectory filtering methods for increasing the accuracy of detection. To validate the proposed algorithms, we use data collected from a small scale testbed, which allows us to generate various training and testing data. This capability makes it possible to study how the different levels of variation in the training data impact the performance of the SVM classification.

Original language	English
Title of host publication	Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5500-5505
Number of pages	6
Edition	March
ISBN (Electronic)	9781479958252
DOIs	https://doi.org/10.1109/WCICA.2014.7053655
State	Published - 2 Mar 2015
Externally published	Yes
Event	2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 - Shenyang, China Duration: 29 Jun 2014 → 4 Jul 2014

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Number	March
Volume	2015-March

Conference

Conference	2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Country/Territory	China
City	Shenyang
Period	29/06/14 → 4/07/14

Access to Document

10.1109/WCICA.2014.7053655

Cite this

Batapati, P., Tran, D., Sheng, W., Liu, M., & Zeng, R. (2015). Video analysis for traffic anomaly detection using support vector machines. In Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014 (March ed., pp. 5500-5505). Article 7053655 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2015-March, No. March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCICA.2014.7053655

Batapati, Praveen ; Tran, Duy ; Sheng, Weihua et al. / Video analysis for traffic anomaly detection using support vector machines. Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March. ed. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 5500-5505 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); March).

@inproceedings{33724268bcad45ef84c71cf9293b0fea,

title = "Video analysis for traffic anomaly detection using support vector machines",

abstract = "In this paper we present a video-based traffic surveillance system which analyzes the video footage and uses the trajectories of the vehicles to detect any anomalous vehicle behavior at a traffic intersection. The trajectory analysis is done using support vector machines (SVMs). We also discuss the trajectory representation and trajectory filtering methods for increasing the accuracy of detection. To validate the proposed algorithms, we use data collected from a small scale testbed, which allows us to generate various training and testing data. This capability makes it possible to study how the different levels of variation in the training data impact the performance of the SVM classification.",

author = "Praveen Batapati and Duy Tran and Weihua Sheng and Meiqin Liu and Ruili Zeng",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 ; Conference date: 29-06-2014 Through 04-07-2014",

year = "2015",

month = mar,

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doi = "10.1109/WCICA.2014.7053655",

language = "英语",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Batapati, P, Tran, D, Sheng, W, Liu, M & Zeng, R 2015, Video analysis for traffic anomaly detection using support vector machines. in Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March edn, 7053655, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), no. March, vol. 2015-March, Institute of Electrical and Electronics Engineers Inc., pp. 5500-5505, 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014, Shenyang, China, 29/06/14. https://doi.org/10.1109/WCICA.2014.7053655

Video analysis for traffic anomaly detection using support vector machines. / Batapati, Praveen; Tran, Duy; Sheng, Weihua et al.
Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March. ed. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5500-5505 7053655 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2015-March, No. March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zeng, Ruili

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N2 - In this paper we present a video-based traffic surveillance system which analyzes the video footage and uses the trajectories of the vehicles to detect any anomalous vehicle behavior at a traffic intersection. The trajectory analysis is done using support vector machines (SVMs). We also discuss the trajectory representation and trajectory filtering methods for increasing the accuracy of detection. To validate the proposed algorithms, we use data collected from a small scale testbed, which allows us to generate various training and testing data. This capability makes it possible to study how the different levels of variation in the training data impact the performance of the SVM classification.

AB - In this paper we present a video-based traffic surveillance system which analyzes the video footage and uses the trajectories of the vehicles to detect any anomalous vehicle behavior at a traffic intersection. The trajectory analysis is done using support vector machines (SVMs). We also discuss the trajectory representation and trajectory filtering methods for increasing the accuracy of detection. To validate the proposed algorithms, we use data collected from a small scale testbed, which allows us to generate various training and testing data. This capability makes it possible to study how the different levels of variation in the training data impact the performance of the SVM classification.

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Batapati P, Tran D, Sheng W, Liu M, Zeng R. Video analysis for traffic anomaly detection using support vector machines. In Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March ed. Institute of Electrical and Electronics Engineers Inc. 2015. p. 5500-5505. 7053655. (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); March). doi: 10.1109/WCICA.2014.7053655

Video analysis for traffic anomaly detection using support vector machines

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