Anomaly detection in low quality traffic monitoring videos using optical flow
Chiman Kwan, Jin Zhou · 2018
This paper summarizes a preliminary study on anomaly detection in low quality traffic monitoring videos. An optical flow based anomaly detection algorithm is proposed to detect anomalies in videos. The algorithm is efficient. Preliminary experiments demonstrate that the proposed algorithm is feasible and has good performance. It should be noted that the anomaly detection algorithm can be used to generate video summaries where the start and end times of anomalies are recorded. In addition, we also developed a user friendly tool that can help operators review video summaries.