Recognition method of traffic violations based on complex interaction between multiple entities

International Journal of Intelligent SystemsVolume 36, Issue 9 p. 5241-5263 RESEARCH ARTICLE Recognition method of traffic violations based on complex interaction between multiple entities Xiaohu Shen, Corresponding Author Xiaohu Shen shenxiaohu@dlmu.edu.cn orcid.org/0000-0002-0206-2622 College of Information Science and Technology, Dalian Maritime University, Dalian, China Department of Forensic Science and Technology, Jiangsu Police Institute, Nanjing, China Correspondence Xiaohu Shen, College of Information Science and Technology, Dalian Maritime University, Dalian, 116026 Liaoning, China. Email: shenxiaohu@dlmu.edu.cnSearch for more papers by this authorJubai An, Jubai An orcid.org/0000-0002-8667-1168 College of Information Science and Technology, Dalian Maritime University, Dalian, ChinaSearch for more papers by this authorZhisong Teng, Zhisong Teng orcid.org/0000-0002-0497-3833 Sub-Bureau of Jiangning, Nanjing Public Security Bureau, Nanjing, ChinaSearch for more papers by this author Xiaohu Shen, Corresponding Author Xiaohu Shen shenxiaohu@dlmu.edu.cn orcid.org/0000-0002-0206-2622 College of Information Science and Technology, Dalian Maritime University, Dalian, China Department of Forensic Science and Technology, Jiangsu Police Institute, Nanjing, China Correspondence Xiaohu Shen, College of Information Science and Technology, Dalian Maritime University, Dalian, 116026 Liaoning, China. Email: shenxiaohu@dlmu.edu.cnSearch for more papers by this authorJubai An, Jubai An orcid.org/0000-0002-8667-1168 College of Information Science and Technology, Dalian Maritime University, Dalian, ChinaSearch for more papers by this authorZhisong Teng, Zhisong Teng orcid.org/0000-0002-0497-3833 Sub-Bureau of Jiangning, Nanjing Public Security Bureau, Nanjing, ChinaSearch for more papers by this author First published: 03 June 2021 https://doi.org/10.1002/int.22511Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Existing methods used in detecting vehicles with traffic violations are mostly based on single-entity frameworks, and those involving multientities remain to be limited. In this paper, we propose a traffic violation detection model, an intelligent vehicle violation recognition method based on multiple entities. It aims to identify vehicles violating the rule of yielding to pedestrians at nonsignalized crosswalks. First, we define the concepts of vehicles and pedestrians and then apply the regression background subtraction method and particle filter algorithm to automatically identify and track moving objects. Second, the pedestrian validity feature template is specified to detect temporal trajectory features from videos with labels and to train classification networks aiming to identify unreasonable behavior patterns, such as remaining on the sidewalk, entering the roadway, bicycle riding, and others. Finally, we develop a novel traffic violation recognition method based on multientity interaction analysis. The cases of failing to yield to pedestrians are recognized based on the multientity feature template built using the proposed method. We verified the effectiveness of the proposed method on a real traffic data set obtained from surveillance cameras. The obtained results are significantly better compared with the baseline method. The area under curve value of the proposed traffic violation recognition method with multientity interaction is 14.5%, 11.1%, and 6.6% higher compared with the three baseline methods based on single-entity frameworks. Volume36, Issue9September 2021Pages 5241-5263 RelatedInformation