Image Understanding for Prevention of Vandalism in Metro Stations
We address here the issues of developing an interpretation system describing automatically human activities from image sequences. The class of applications we are interested in, is the automatic surveillance and monitoring of metro stations scenes observed by a monocular camera. Given image sequences, an interpretation system has to recognize scenarios relative to the behaviours of mobile objects detected in the scene. In our case, the mobile objects correspond to humans and the scenarios describe human activities.
KeywordsVideo Sequence Mobile Object Interpretation System Metro Station Mobile Region
Unable to display preview. Download preview PDF.
- A. Bobick and J. Davis, “Real—time recognition of activity using temporal templates”, in proc. of the Workshop on Applications of Computer Vision, december 1996Google Scholar
- M. Brand and N. Oliver and A. Pentland, “Coupled hidden Markov models for complex action recognition”, in proc. of CVPR, Puerto Rico, USA, 1997Google Scholar
- F. Bremond and M. Thonnat, “Issues of representing context illustrated by video — surveillance applications”, in International Journal of Human-Computer Studies, Special Issue on Context, 1998, volume 48, pp375-391.Google Scholar
- F. Bremond and M. Thonnat, “Tracking multiple non-rigid objects in video sequences”, in IEEE Journal on Transactions On Automatic Control, 1998 to be publishedGoogle Scholar
- C. Castel and L. Chaudron and C. Tessier, “What is going on? A high level interpretation of sequences of images”, in Proc. of the ECCV96 workshop on Conceptual Descriptions from Images, University of Cambridge, April 1996Google Scholar
- N. Chleq and M. Thonnat, “Realtime image sequence interpretation for video—surveillance applications”, in Proc. of ICIP 96, Lausanne, September 1996Google Scholar
- A. Galton, “Towards an Integrated Logic of Space, Time and Motion”, in International Joint Conference on Artificial Intelligence (IJCAI), Chambery, France August 1993Google Scholar
- R. Howarth, “Spatial representation, reasoning and control for a surveillance system”, in PhD Thesis, Queen Mary and Westfield College, July 1994Google Scholar
- B. Neumann, in Semantic structures: advances in natural language processing, chapter 5, pp 167-206, David L. Waltz, 1989Google Scholar
- A. Teschioni and C. Regazzoni, “Performances Evaluation Strategies of an Image Processing System for Surveillance Applications”, in Proc. of Workshop on Advanced Video—based Surveillance Systems, Genova, Italy, April 1998Google Scholar