W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIGMA: A Knowledge-Based Aerial Image Understanding System
SIGMA: A Knowledge-Based Aerial Image Understanding System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gesture recognition using the Perseus architecture
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Tracking appearances with occlusions
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Unconstrained multiple-people tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Understanding dynamic scenes based on human sequence evaluation
Image and Vision Computing
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High-level understanding of motion events is a critical task in any system which aims to analyse dynamic human-populated scenes. However, current tracking techniques still do not address complex interaction events among multiple targets. In this paper, a principled event-management framework is proposed, and it is included in a hierarchical and modular tracking architecture. Multiple-target interaction events, and a proper scheme for tracker instantiation and removal according to scene events, are considered. Multiple-target group management allows the system to switch among different operation modes. Robust and accurate tracking results have been obtained in both indoor and outdoor scenarios, without considering a-priori knowledge about either the scene or the targets based on a previous training period.