  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
