TY - CONF T1 - An improved mean shift tracking method based on nonparametric clustering and adaptive bandwidth T2 - Machine Learning and Cybernetics, 2008 International Conference on Y1 - 2008 A1 - Zhuolin Jiang A1 - Li,Shao-Fa A1 - Jia,Xi-Ping A1 - Zhu,Hong-Li KW - adaptive bandwidth KW - appearance model KW - bandwidth matrix KW - Bhattacharyya coefficient KW - color information KW - color space partitioning KW - image colour analysis KW - iterative procedure KW - kernel bandwidth parameter KW - kernel density estimate KW - log-likelihood function KW - mean shift tracking method KW - modified weight function KW - nonparametric clustering KW - Object detection KW - object representation KW - object tracking KW - pattern clustering KW - similarity measure KW - spatial layout KW - target candidate KW - target model KW - tracking AB - An improved mean shift method for object tracking based on nonparametric clustering and adaptive bandwidth is presented in this paper. Based on partitioning the color space of a tracked object by using a modified nonparametric clustering, an appearance model of the tracked object is built. It captures both the color information and spatial layout of the tracked object. The similarity measure between the target model and the target candidate is derived from the Bhattacharyya coefficient. The kernel bandwidth parameters are automatically selected by maximizing the lower bound of a log-likelihood function, which is derived from a kernel density estimate using the bandwidth matrix and the modified weight function. The experimental results show that the method can converge in an average of 2.6 iterations per frame. JA - Machine Learning and Cybernetics, 2008 International Conference on VL - 5 M3 - 10.1109/ICMLC.2008.4620880 ER -