Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Multiple Object Class Detection with a Generative Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multi-Target Tracking - Linking Identities using Bayesian Network Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing interaction between human performers using 'key pose doublet'
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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This paper presents a solution to the problem of tracking people within crowded scenes. The aim is to maintain individual object identity through a crowded scene which contains complex interactions and heavy occlusions of people. Our approach uses the strengths of two separate methods; a global object detector and a localised frame by frame tracker. A temporal relationship model of torso detections built during low activity period, is used to further disambiguate during periods of high activity. A single camera with no calibration and no environmental information is used. Results are compared to a standard tracking method and groundtruth. Two video sequences containing interactions, overlaps and occlusions between people are used to demonstrate our approach. The results show that our technique performs better that a standard tracking method and can cope with challenging occlusions and crowd interactions.