Towards model-based recognition of human movements in image sequences
CVGIP: Image Understanding
Automatic symbolic traffic scene analysis using belief networks
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and robust human detection and tracking based on omega-shape features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Unconstrained multiple-people tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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Multiple-target tracking represents a challenging question in uncontrolled scenarios. Due to high-level applications, such as behavioral analysis, the need of a robust tracking system is high. In a multiple tracking scenario it is necessary to consider and resolve occlusions, as well as formations and splitting of object groups. In this work, a method based in a hierarchical architecture for multiple tracking is proposed to deal with these matters. Background subtraction, blob detection, low-level tracking, collision detection and high-level appearance tracking is used to avoid occlusion and grouping problems. Experimental results show promising results in tracking management, grouping, splitting, occlusion events, while remains invariant to illumination changes.