  Recognizing abnormal events such as traffic violations and accidents in
natural driving scenes is essential for successful autonomous driving and
advanced driver assistance systems. However, most work on video anomaly
detection suffers from two crucial drawbacks. First, they assume cameras are
fixed and videos have static backgrounds, which is reasonable for surveillance
applications but not for vehicle-mounted cameras. Second, they pose the problem
as one-class classification, relying on arduously hand-labeled training
datasets that limit recognition to anomaly categories that have been explicitly
trained. This paper proposes an unsupervised approach for traffic accident
detection in first-person (dashboard-mounted camera) videos. Our major novelty
is to detect anomalies by predicting the future locations of traffic
participants and then monitoring the prediction accuracy and consistency
metrics with three different strategies. We evaluate our approach using a new
dataset of diverse traffic accidents, AnAn Accident Detection (A3D), as well as
another publicly-available dataset. Experimental results show that our approach
outperforms the state-of-the-art.
