Kernel based articulated object tracking with scale adaptation and model update
Anbang Yao; Guijin Wang; Xinggang Lin; Hao Wang
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Volume , Issue , March 31 2008-April 4 2008 Page(s):945 - 948
Digital Object Identifier   10.1109/ICASSP.2008.4517767
Summary:Kernel based object tracking (KBOT) is one of the most popular and effective techniques for tracking task. However the constancy of the target model and unsound scale adaptation method are two main limitations. In this paper, we present a kernel based approach incorporated with scale estimation and target model update for articulated object tracking task. After predicating the object center with scale fixed KBOT, we extend scale selection theory to estimate the local optimal object scale. Once the object scale has been estimated, a kernel density estimation based strategy is developed to update the target model. Experimental results show that our approach is superior to traditional KBOT in the following two aspects: 1) it is less affected by the object scale change; 2) it is less prone to appearance variation.

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