Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Elements of information theory
Elements of information theory
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tracking multiple objects in the presence of articulated and occluded motion
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose visual tracking of multiple objects (faces of people) in a meeting scenario based on low-level features such as skin-color, target motion, and target size. Based on these features automatic initialization and termination of objects is performed. Furthermore, on-line learning is used to incrementally update the models of the tracked objects to reflect the appearance changes. For tracking a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach.