A Three-Stage Anomaly Detection Framework for Traffic Videos
As reported by the United Nations in 2021, road accidents cause 1.3 million deaths and 50 million injuries worldwide each year. Detecting traffic anomalies timely and taking immediate emergency response and rescue measures are essential to reduce casualties, economic losses, and traffic congestion....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/9463559 |
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| _version_ | 1832558373634048000 |
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| author | Junzhou Chen Jiancheng Wang Jiajun Pu Ronghui Zhang |
| author_facet | Junzhou Chen Jiancheng Wang Jiajun Pu Ronghui Zhang |
| author_sort | Junzhou Chen |
| collection | DOAJ |
| description | As reported by the United Nations in 2021, road accidents cause 1.3 million deaths and 50 million injuries worldwide each year. Detecting traffic anomalies timely and taking immediate emergency response and rescue measures are essential to reduce casualties, economic losses, and traffic congestion. This paper proposed a three-stage method for video-based traffic anomaly detection. In the first stage, the ViVit network is employed as a feature extractor to capture the spatiotemporal features from the input video. In the second stage, the class and patch tokens are fed separately to the segment-level and video-level traffic anomaly detectors. In the third stage, we finished the construction of the entire composite traffic anomaly detection framework by fusing outputs of two traffic anomaly detectors above with different granularity. Experimental evaluation demonstrates that the proposed method outperforms the SOTA method with 2.07% AUC on the TAD testing overall set and 1.43% AUC on the TAD testing anomaly subset. This work provides a new reference for traffic anomaly detection research. |
| format | Article |
| id | doaj-art-28784bc89727444b9885a687ce052f4f |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-28784bc89727444b9885a687ce052f4f2025-02-03T01:32:31ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/9463559A Three-Stage Anomaly Detection Framework for Traffic VideosJunzhou Chen0Jiancheng Wang1Jiajun Pu2Ronghui Zhang3School of Intelligent Systems EngineeringSchool of Intelligent Systems EngineeringSchool of Intelligent Systems EngineeringSchool of Intelligent Systems EngineeringAs reported by the United Nations in 2021, road accidents cause 1.3 million deaths and 50 million injuries worldwide each year. Detecting traffic anomalies timely and taking immediate emergency response and rescue measures are essential to reduce casualties, economic losses, and traffic congestion. This paper proposed a three-stage method for video-based traffic anomaly detection. In the first stage, the ViVit network is employed as a feature extractor to capture the spatiotemporal features from the input video. In the second stage, the class and patch tokens are fed separately to the segment-level and video-level traffic anomaly detectors. In the third stage, we finished the construction of the entire composite traffic anomaly detection framework by fusing outputs of two traffic anomaly detectors above with different granularity. Experimental evaluation demonstrates that the proposed method outperforms the SOTA method with 2.07% AUC on the TAD testing overall set and 1.43% AUC on the TAD testing anomaly subset. This work provides a new reference for traffic anomaly detection research.http://dx.doi.org/10.1155/2022/9463559 |
| spellingShingle | Junzhou Chen Jiancheng Wang Jiajun Pu Ronghui Zhang A Three-Stage Anomaly Detection Framework for Traffic Videos Journal of Advanced Transportation |
| title | A Three-Stage Anomaly Detection Framework for Traffic Videos |
| title_full | A Three-Stage Anomaly Detection Framework for Traffic Videos |
| title_fullStr | A Three-Stage Anomaly Detection Framework for Traffic Videos |
| title_full_unstemmed | A Three-Stage Anomaly Detection Framework for Traffic Videos |
| title_short | A Three-Stage Anomaly Detection Framework for Traffic Videos |
| title_sort | three stage anomaly detection framework for traffic videos |
| url | http://dx.doi.org/10.1155/2022/9463559 |
| work_keys_str_mv | AT junzhouchen athreestageanomalydetectionframeworkfortrafficvideos AT jianchengwang athreestageanomalydetectionframeworkfortrafficvideos AT jiajunpu athreestageanomalydetectionframeworkfortrafficvideos AT ronghuizhang athreestageanomalydetectionframeworkfortrafficvideos AT junzhouchen threestageanomalydetectionframeworkfortrafficvideos AT jianchengwang threestageanomalydetectionframeworkfortrafficvideos AT jiajunpu threestageanomalydetectionframeworkfortrafficvideos AT ronghuizhang threestageanomalydetectionframeworkfortrafficvideos |