Efficient anomaly detection algorithms for summarizing low quality videos
Chiman Kwan, Jin Zhou, Zheshen Wang, LI Bao-xin · 2018
Many surveillance and security monitoring videos are long and of low quality. Moreover, reviewing and extracting anomaly events in the videos is a lengthy and manually intensive process. In this paper, we present two efficient anomaly detection algorithms based on saliency to detect anomalous events in low quality videos. The events’ start times and durations are saved in a video summary for later reviews. The video summary is very short. For example, we have summarized a 14-minute long video into a 16-second video summary. Extensive evaluations of the two algorithms clearly demonstrated the feasibility of these algorithms. A user friendly software tool has also been developed to help human operators review and confirm those events.