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

• URL: http://arxiv.org/abs/2004.03044v1
• Date: Mon, 6 Apr 2020 23:58:59 GMT
• Title: When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos
• Authors: Yu Yao, Xizi Wang, Mingze Xu, Zelin Pu, Ella Atkins, David Crandall
• Abstract summary: This paper proposes traffic anomaly detection with a textitwhen-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.
• Score: 9.638503179434581
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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