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.

Original languageEnglish (US)
Title of host publicationPattern Recognition and Tracking XXIX
PublisherSPIE
Volume10649
ISBN (Electronic)9781510618091
DOIs
StatePublished - Jan 1 2018
EventPattern Recognition and Tracking XXIX 2018 - Orlando, United States
Duration: Apr 18 2018Apr 19 2018

Other

OtherPattern Recognition and Tracking XXIX 2018
CountryUnited States
CityOrlando
Period4/18/184/19/18

Keywords

  • Anomaly detection
  • low quality videos
  • video summarization

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Kwan, C., Zhou, J., Wang, Z., & Li, B. (2018). Efficient anomaly detection algorithms for summarizing low quality videos. In Pattern Recognition and Tracking XXIX (Vol. 10649). [1064906] SPIE. https://doi.org/10.1117/12.2303764