An information retrieval approach to identifying infrequent events in surveillance video

Suzanne Little, Iveel Jargalsaikhan, Cem Direkoglu, Noel E. O'Connor, Alan F. Smeaton, Kathy Clawson, Hao Li, Jun Liu, Bryan Scotney, Hui Wang, Marcos Nieto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

This paper presents work on integrating multiple computer vision-based approaches to surveillance video analysis to support user retrieval of video segments showing human activities. Applied computer vision using real-world surveillance video data is an extremely challenging research problem, independently of any information retrieval (IR) issues. Here we describe the issues faced in developing both generic and specific analysis tools and how they were integrated for use in the new TRECVid interactive surveillance event detection task. We present an interaction paradigm and discuss the outcomes from face-to-face end user trials and the resulting feedback on the system from both professionals, who manage surveillance video, and computer vision or machine learning experts. We propose an information retrieval approach to finding events in surveillance video rather than solely relying on traditional annotation using specifically trained classifiers.
Original languageEnglish
Title of host publicationICMR 2013 - Proceedings of the 3rd ACM International Conference on Multimedia Retrieval
Pages223-230
Number of pages8
DOIs
Publication statusPublished - 16 Apr 2013
Externally publishedYes

Publication series

NameProceedings of the ACM International Conference on Multimedia Retrieval

Bibliographical note

3rd ACM International Conference on Multimedia Retrieval, ICMR 2013 ; Conference date: 16-04-2013 Through 20-04-2013

Keywords

  • surveillance event detection
  • video analysis

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