An information retrieval approach to identifying infrequent events in surveillance video


Little S., Jargalsaikhan I., Direkoglu C., O'Connor N. E., Smeaton A. F., Clawson K., ...More

3rd ACM International Conference on Multimedia Retrieval, ICMR 2013, Dallas, TX, United States Of America, 16 - 20 April 2013, pp.223-230, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1145/2461466.2461503
  • City: Dallas, TX
  • Country: United States Of America
  • Page Numbers: pp.223-230
  • Keywords: surveillance event detection, video analysis
  • Middle East Technical University Northern Cyprus Campus Affiliated: No

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. © 2013 ACM.