@article {13229, title = {Incremental density approximation and kernel-based bayesian filtering for object tracking}, journal = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition}, volume = {1}, year = {2004}, month = {2004///}, abstract = {Statistical density estimation techniques are used in manycomputer vision applications such as object tracking, back- ground subtraction, motion estimation and segmentation. The particle filter (Condensation) algorithm provides a gen- eral framework for estimating the probability density func- tions (pdf) of general non-linear and non-Gaussian systems. However, since this algorithm is based on a Monte Carlo ap- proach, where the density is represented by a set of random samples, the number of samples is problematic, especially for high dimensional problems. In this paper, we propose an alternative to the classical particle filter in which the un- derlying pdf is represented with a semi-parametric method based on a mode finding algorithm using mean-shift. A mode propagation technique is designed for this new representa- tion for tracking applications. A quasi-random sampling method [14] in the measurement stage is used to improve performance, and sequential density approximation for the measurements distribution is performed for efficient compu- tation. We apply our algorithm to a high dimensional color- based tracking problem, and demonstrate its performance by showing competitive results with other trackers. }, author = {Han,B. and Comaniciu, D. and Zhu,Y. and Davis, Larry S.} }