Feature tracking with automatic selection of spatial scales
Computer Vision and Image Understanding
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real time hand tracking by combining particle filtering and mean shift
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Joint feature correspondences and appearance similarity for robust visual object tracking
IEEE Transactions on Information Forensics and Security
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We propose a novel scheme that jointly employs anisotropic mean shift and particle filters for tracking moving objects from video. The proposed anisotropic mean shift, that is applied to partitioned areas in a candidate object bounding box whose parameters (center, width, height and orientation) are adjusted during the mean shift iterations, seeks multiple local modes in spatial-kernel weighted color histograms. By using a Gaussian distributed Bhattacharyya distance as the likelihood and mean shift updated parameters as the state vector, particle filters become more efficient in terms of tracking using a small number of particles (