CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Interacting Multiple Model Particle Filter to Adaptive Visual Tracking
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Efficient Mean-Shift Tracking via a New Similarity Measure
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An improvement in MSEPF for visual tracking
Artificial Life and Robotics
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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Recently, particle filter has been applied to many visual tracking problems and it has been modified in order to reduce the computation time or memory usage. One of them is the Mean-Shift embedded particle filter (MSEPF, for short) and it is further modified as Randomized MSEPF. These methods can decrease the number of the particles without the loss of tracking accuracy. However, the accuracy may depend on the definition of the likelihood function (observation model) and of the prediction model. In this paper, the authors propose an extension of these models in order to increase the tracking accuracy. Furthermore, the expansion resetting method, which was proposed for mobile robot localization, and the changing the size of the window in Mean-Shift search are also selectively applied in order to treat the occlusion or rapid change of the movement.