Unsupervised activity analysis and monitoring algorithms for effective surveillance systems
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Combining multiple sensors for event recognition of older people
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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This paper presents an approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior. This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence. First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm. A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language. The group events recognition approach is successfully validated on 4 camera views from 3 datasets: an airport, a subway, a shopping center corridor and an entrance hall.