Target-driven video summarization in a camera network

Shen Chi Chen, Kevin Lin, Shih Yao Lin, Kuan-Wen Chen, Chih Wei Lin, Chu Song Chen, Yi Ping Hung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Nowadays, ever expanding camera network makes it difficult to find the suspect from lengthy video records. This paper proposes a target-driven video summarization framework which provides two-step Filtered Summarized Video (FSV) for tracing suspects. Before the target is identified, users can find the target efficiently using the firststep FSV of any arbitrary camera. The first-step FSV filters all the attributes of the target including the time information and the target's categories. After identifying the target, the second-step FSV with additional spatio-temporal & appearance cues are triggered in the neighbor cameras. To enhance the accuracy of the object classification for FSV, we propose a Perspective Dependent Model (PDM) which consists of many grid-based models. Finally, the experimental results show that grid-based model is more robust than general detectors and the user study demonstrates better performance for target finding and tracking in camera network for surveillance.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages3577-3581
Number of pages5
DOIs
StatePublished - 1 Dec 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 15 Sep 201318 Sep 2013

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period15/09/1318/09/13

Keywords

  • camera network
  • object classification
  • video summarization
  • video surveillance

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