An information retrieval approach to identifying infrequent events in surveillance video

• DOI: 10.1145/2461466.2461503
• Publication Date: 2013-04-16T13:51:28Z
• Authors: Suzanne Little; Iveel Jargalsaikhan; Kathy Clawson; Marcos Nieto; Hao Li; Cem Direkoglu; Noel E. O'Connor; Alan F. Smeaton; Bryan Scotney; Hui Wang; Jun Liu
• Keywords: Video retrieval

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