Abstract
Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous and advanced driver assistance systems. However, most work on video anomaly detection suffers from one of two crucial drawbacks. First, it assumes cameras are fixed and videos have a static background, which is reasonable for surveillance applications but not for vehicle-mounted cameras. Second, it poses the problem as one-class classification, which relies on arduous human annotation and only recognizes categories of anomalies that have been explicitly trained. In this paper, we propose an unsupervised approach for traffic accident detection in first-person 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. To evaluate our approach, we introduce 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.
Abstract (translated)
URL
https://arxiv.org/abs/1903.00618
