Bidirectional Spatio-Temporal Feature Learning With Multiscale Evaluation for Video Anomaly Detection

Zhong, Yuanhong; Chen, Xia; Hu, Yongting; Tang, Panliang; Ren, Fan

doi:10.1109/tcsvt.2022.3190539

Cited by 21 publications

(1 citation statement)

References 42 publications

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“…Compared with video anomaly detection [45], [26], [46], [47], [48], vision-based traffic accident perception shows the most inadequate research mainly due to the collection difficulty of accident video data. Most relating to this work, various surveys on traffic accident situations concentrate on traffic accident recognition or collision avoidance from specific occasions (e.g., intersection, urban scene), road participants (e.g., vehicles-centric and pedestrian-centric), and applications (e.g., surveillance safety [38], [39] and autonomous driving [43], [44]).…”

Section: Distinction From Other Reviewsmentioning

confidence: 99%

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

Fang

Qiao²,

Bai³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Traffic accident detection and anticipation is an obstinate road safety problem and painstaking efforts have been devoted. With the rapid growth of video data, Vision-based Traffic Accident Detection and Anticipation (named Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving and surveillance safety. However, the long-tailed, unbalanced, highly dynamic, complex, and uncertain properties of traffic accidents form the Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI development may focus on these OOD but important problems. What has been done for Vision-TAD and Vision-TAA? What direction we should focus on in the future for this problem? A comprehensive survey is important. We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA. The pros and cons of each research prototype are discussed in detail during the investigation. In addition, we also provide a critical review of 31 publicly available benchmarks and related evaluation metrics. Through this survey, we want to spawn new insights and open possible trends for Vision-TAD and Vision-TAA tasks.

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