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Video anomaly detection (VAD) has been extensively studied. However, researchon egocentric traffic videos with dynamic scenes lacks large-scale benchmarkdatasets as well as effective evaluation metrics. This paper proposes trafficanomaly detection with a \textit{when-where-what} pipeline to detect, localize,and recognize anomalous events from egocentric videos. We introduce a newdataset called Detection of Traffic Anomaly (DoTA) containing 4,677 videos withtemporal, spatial, and categorical annotations. A new spatial-temporal areaunder curve (STAUC) evaluation metric is proposed and used with DoTA.State-of-the-art methods are benchmarked for two VAD-related tasks.Experimentalresults show STAUC is an effective VAD metric. To our knowledge, DoTA is thelargest traffic anomaly dataset to-date and is the first supporting trafficanomaly studies across when-where-what perspectives. Our code and dataset canbe found in: https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly

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