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Time-stamp aware anomaly detection in traffic videos is an essential task forthe advancement of the intelligent transportation system. Anomaly detection invideos is a challenging problem due to sparse occurrence of anomalous events,inconsistent behavior of a different type of anomalies and imbalanced availabledata for normal and abnormal scenarios. In this paper, we present a three-stagepipeline to learn the motion patterns in videos to detect a visual anomaly.First, the background is estimated from recent history frames to identify themotionless objects. This background image is used to localize thenormal/abnormal behavior within the frame. Further, we detect an object ofinterest in the estimated background and categorize it into anomaly based on atime-stamp aware anomaly detection algorithm. We also discuss the challengesfaced in improving performance over the unseen test data for traffic anomalydetection. Experiments are conducted over Track 3 of NVIDIA AI city challenge2019. The results show the effectiveness of the proposed method in detectingtime-stamp aware anomalies in traffic/road videos.

Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos