Content-Based Video Retrieval (CBVR) System for CCTV Surveillance Videos

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.