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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Neural Networks: A Comprehensive Foundation
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Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
A Framework for Model-Based Tracking Experiments in Image Sequences
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Contour graph based human tracking and action sequence recognition
Pattern Recognition
Multi-cue-based CamShift guided particle filter tracking
Expert Systems with Applications: An International Journal
Multiple-person tracker with a fixed slanting stereo camera
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Efficient object tracking by condentional and cascaded image sensing
Computer Standards & Interfaces
Multiple human tracking in high-density crowds
Image and Vision Computing
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The CONDENSATION (Conditional Density Propagation) algorithm has a robust tracking performance and suitability for real-time implementation. However, the CONDENSATION tracker has some difficulties with real-time implementation for multiple people tracking since it requires very complicated shape modelling and a large number of samples for precise tracking performance. Further, it shows a poor tracking performance in the case of close or partially occluded people. To overcome these difficulties, we present three improvements: First, we construct effective templates of people's shapes using the SOM (Self-Organizing Map). Second, we take the discrete HMM (Hidden Markov Modelling) for an accurate dynamical model of the people's shape transition. Third, we use the competition rule to separate close or partially occluded people effectively. Simulation results shows that the proposed CONDENSATION algorithm can achieve robust and real-time tracking in the image sequences of a crowd of people.