Efficient anomaly detection algorithms for summarizing low quality videos

Chiman Kwan; Jin Zhou; Zheshen Wang; Baoxin Li

doi:10.1117/12.2303764

27 April 2018 Efficient anomaly detection algorithms for summarizing low quality videos

Chiman Kwan, Jin Zhou, Zheshen Wang, Baoxin Li

Proceedings Volume 10649, Pattern Recognition and Tracking XXIX; 1064906 (2018) https://doi.org/10.1117/12.2303764
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Chiman Kwan, Jin Zhou, Zheshen Wang, and Baoxin Li "Efficient anomaly detection algorithms for summarizing low quality videos", Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 1064906 (27 April 2018); https://doi.org/10.1117/12.2303764

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