Content-Based Video Retrieval (CBVR) system for CCTV surveillance videos

Yang, Yan, Lovell, Brian C. and Dadgostar, Farhad (2009). Content-Based Video Retrieval (CBVR) system for CCTV surveillance videos. In: Hao Shi, DICTA 2009 : 2009 digital image computing techniques and applications : proceedings. Digital Image Computing: Techniques and Applications, DICTA 2009, Melbourne, VIC Australia, (183-187). 1 - 3 December 2009. doi:10.1109/DICTA.2009.36

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Author Yang, Yan
Lovell, Brian C.
Dadgostar, Farhad
Title of paper Content-Based Video Retrieval (CBVR) system for CCTV surveillance videos
Conference name Digital Image Computing: Techniques and Applications, DICTA 2009
Conference location Melbourne, VIC Australia
Conference dates 1 - 3 December 2009
Proceedings title DICTA 2009 : 2009 digital image computing techniques and applications : proceedings
Place of Publication Piscataway, NJ United States
Publisher I E E E
Publication Year 2009
Year available 2009
Sub-type Fully published paper
DOI 10.1109/DICTA.2009.36
Open Access Status
ISBN 9780769538662
9781424452972
Editor Hao Shi
Start page 183
End page 187
Total pages 5
Collection year 2010
Language eng
Abstract/Summary The inherent nature of image and video and its multi-dimension data space makes its processing and interpretation a very complex task, normally requiring considerable processing power. Moreover, understanding the meaning of video content and storing it in a fast searchable and readable form, requires taking advantage of image processing methods, which when running them on a video stream per query, would not be cost effective, and in some cases is quite impossible due to time restrictions. Hence, to speed up the search process, storing video and its extracted meta-data together is desired. The storage model itself is one of the challenges in this context, as based on the current CCTV technology; it is estimated to require a petabyte size data management system. This estimate however, is expected to grow rapidly as current advances in video recording devices are leading to higher resolution sensors, and larger frame size. On the other hand, the increasing demand for object tracking on video streams has invoked the research on Content-Based Image Retrieval (CBIR) and Content-Based Video Retrieval (CBVR). In this paper, we present the design and implementation of a framework and a data model for CCTV surveillance videos on RDBMS which provides the functions of a surveillance monitoring system, with a tagging structure for event detection. On account of some recent results, we believe this is a promising direction for surveillance video search in comparison to the existing solutions.
Subjects 1703 Computational Theory and Mathematics
1704 Computer Graphics and Computer-Aided Design
1712 Software
Keyword Content-based video retrieval
Intelligent CCTV
Surveillance video database
Video frame tagging
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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