  Time-stamp aware anomaly detection in traffic videos is an essential task for
the advancement of the intelligent transportation system. Anomaly detection in
videos is a challenging problem due to sparse occurrence of anomalous events,
inconsistent behavior of a 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 a 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 an 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.
