When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos

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Authors David Crandall, Yu Yao, Xizi Wang, Zelin Pu, Mingze Xu, Ella Atkins
Journal/Conference Name arXiv preprint
Paper Category
Paper Abstract Video anomaly detection (VAD) has been extensively studied. However, research on egocentric traffic videos with dynamic scenes lacks large-scale benchmark datasets as well as effective evaluation metrics. This paper proposes traffic anomaly detection with a \textit{when-where-what} pipeline to detect, localize, and recognize anomalous events from egocentric videos. We introduce a new dataset called Detection of Traffic Anomaly (DoTA) containing 4,677 videos with temporal, spatial, and categorical annotations. A new spatial-temporal area under curve (STAUC) evaluation metric is proposed and used with DoTA. State-of-the-art methods are benchmarked for two VAD-related tasks.Experimental results show STAUC is an effective VAD metric. To our knowledge, DoTA is the largest traffic anomaly dataset to-date and is the first supporting traffic anomaly studies across when-where-what perspectives. Our code and dataset can be found in https//github.com/MoonBlvd/Detection-of-Traffic-Anomaly
Date of publication 2020
Code Programming Language Multiple
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