Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos

Kuldeep Marotirao Biradar, Ayushi Gupta, Murari Mandal, Santosh Kumar Vipparthi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 13-20

Time-stamp aware anomaly detection in traffic videos is an essential task for the advancement of intelligent transportation system. Anomaly detection in videos is a challenging problem due to sparse occurrence of anomalous events, inconsistent behavior of different type of anomalies and imbalanced available data for normal and abnormal scenarios. In this paper we present a three-stage pipeline to learn the motion patterns in videos to detect visual anomaly. First, the background is estimated from recent history frames to identify the motionless objects. This background image is used to localize the normal/abnormal behavior within the frame. Further, we detect object of interest in the estimated background and categorize it into anomaly based on a time-stamp aware anomaly detection algorithm. We also discuss the challenges faced in improving performance over the unseen test data for traffic anomaly detection. Experiments are conducted over Track 3 of NVIDIA AI city challenge 2019. The results show the effectiveness of the proposed method in detecting time-stamp aware anomalies in traffic/road videos.