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An information retrieval approach to identifying infrequent events in surveillance video
Little, Suzanne; Jargalsaikhan, Iveel; Clawson, Kathy; Li, Hao; Nieto, Marcos; Direkoglu, Cem; O'Connor, Noel E.; Smeaton, Alan F.; Liu, Jun; Scotney, Bryan; Wang, Hui
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.
Keyword(s): Information storage and retrieval systems; Digital video; Information retrieval; TRECVid; surveillance event detection
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Little, Suzanne ORCID: 0000-0003-3281-3471 <>, Jargalsaikhan, Iveel, Clawson, Kathy, Li, Hao, Nieto, Marcos, Direkoglu, Cem, O'Connor, Noel E. ORCID: 0000-0002-4033-9135 <>, Smeaton, Alan F. ORCID: 0000-0003-1028-8389 <>, Liu, Jun, Scotney, Bryan and Wang, Hui (2013) An information retrieval approach to identifying infrequent events in surveillance video. In: ACM International Conference on Multimedia Retrieval, 16-19 Apr. 2013, Dallas, TX.
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First Indexed: 2013-04-19 05:08:45 Last Updated: 2019-02-09 06:38:08