CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
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Mean shift tracking fails when the velocity of target is so large that the target's window kernel in the previous frame can not cover the target in the current frame. Combination of mean shift and single Kalman filter also fails when the target's velocity changed suddenly. To deal with the problem of tracking image target that has large and changing velocity, an efficient image tracking method integrated mean shift and double model filters is proposed. Two motion models can switch each other by using a probabilistic likelihood. Experiment results show the method integrated mean shift and double model filters can successfully keep tracking target, no matter the target's velocity is large or small, changing or constant, with modest requirement of computation resource.