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An information retrieval approach to identifying infrequent events in surveillance video

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

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:TRECVid; surveillance event detection
Subjects:Computer Science > Information storage and retrieval systems
Computer Science > Digital video
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number 285621, project titled SAVASA.
ID Code:17820
Deposited On:18 Apr 2013 14:13 by Suzanne Little. Last Modified 09 Feb 2017 11:54

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