Real-Time Detection of Passing Objects Using Virtual Gate and Motion Vector Analysis
UIC '08 Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing
A Method of Counting Pedestrians in Crowded Scenes
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Example based learning for object detection in images
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
An architecture for a self configurable video supervision
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Help From Strangers --Media Arts In Ambient Intelligence Research
Proceedings of the 2007 conference on Advances in Ambient Intelligence
Multi-cue Based Visual Tracking in Clutter Scenes with Occlusions
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Pedestrian Identification with Distance Transform and Hierarchical Search Tree
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
A visitor counter system using fuzzy measure theory and boosting method
WSEAS Transactions on Information Science and Applications
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This paper describes a vision based pedestrian detection and tracking system which is able to count people in very crowded situations like escalator entrances in underground stations. The proposed system uses motion to compute regions of interest and prediction of movements, extracts shape information from the video frames to detect individuals, and applies texture features to recognize people. A search strategy creates trajectories and new pedestrian hypotheses and then filters and combines those into accurate counting events. We show that counting accuracies up to 98 % can be achieved.