A novel fast and effictive video retrieval system for surveillance application

Fast and effective indexing and retrieval from large amount of surveillance videos are very important issues. This paper proposes a novel object-semantic-based surveillance video indexing and retrieval system, which is mainly composed of two modules: video analysis and video retrieval. In the video analysis, the systems first segments video objects (VO) from surveillance videos, and the fundamental semantic information is then extracted and indexed into the database. A normal approach of Gaussian Mixed Model (GMM) is applied in video object extraction (VOE) and video object segmentation (VOS). During retrieval, the query is converted to semantic information without re-processing the surveillance videos. Color, edge orientation histograms and SIFT (Scale Invariant Feature Transforms), as the key features and similarity measurement, are considered together to accurately match the video objects (VOM). The experiment shows that a user can retrieve the required videos effectively.