Object Tracking by Anisotropic Kernel Mean Shift

Qi Su-min; Huang Xian-wu; Yi Huai-feng

doi:10.3724/SP.J.1146.2005.00928

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Qi Su-min, Huang Xian-wu, Yi Huai-feng. Object Tracking by Anisotropic Kernel Mean Shift[J]. Journal of Electronics & Information Technology, 2007, 29(3): 686-689. doi: 10.3724/SP.J.1146.2005.00928

Citation:

Qi Su-min, Huang Xian-wu, Yi Huai-feng. Object Tracking by Anisotropic Kernel Mean Shift[J]. Journal of Electronics & Information Technology, 2007, 29(3): 686-689. doi: 10.3724/SP.J.1146.2005.00928

Qi Su-min, Huang Xian-wu, Yi Huai-feng. Object Tracking by Anisotropic Kernel Mean Shift[J]. Journal of Electronics & Information Technology, 2007, 29(3): 686-689. doi: 10.3724/SP.J.1146.2005.00928

Citation:

Qi Su-min, Huang Xian-wu, Yi Huai-feng. Object Tracking by Anisotropic Kernel Mean Shift[J]. Journal of Electronics & Information Technology, 2007, 29(3): 686-689. doi: 10.3724/SP.J.1146.2005.00928

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Object Tracking by Anisotropic Kernel Mean Shift

doi: 10.3724/SP.J.1146.2005.00928

Qi Su-min^{①②;黄贤武},
Huang Xian-wu,
Yi Huai-feng

Received Date: 2005-07-28
Rev Recd Date: 2005-12-29
Publish Date: 2007-03-19

Abstract

Abstract

Mean shift, an iterative procedure that shifts each data point to the average of data points in its neighborhood, has been applied to object tracking. However, with the changing structure of object in video sequences, traditional mean shift tracker by isotropic kernel often loses the object, especially when object structure varies fast. This paper implements object tracking with anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the changing object structure. The algorithm ensures tracking robust and real-time. Experimental results show it is effective.

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References

[1] Bretzner L and Lindeberg T. Feature tracking with automatic selection of spatial scales[J].Computer Vision and Image Understanding.1998, 71(3):385-392 [2] Cheng Yizong. Mean shift, mode seeking, and clustering[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.1995, 17(8):790-799 [3] Comaniciu D, Ramesh V, and Meer P. Kernel-based object tracking[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.2003, 25(5):564-577 [4] Zivkovic Z and Krose B. An EM-like algorithm for color- histogram- based object tracking[C][J].Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington DC, USA.2004, vol.1:798-803 [5] Chen H F and Meer P. Robust computer vision through kernel density estimation[C]. Computer Vision - ECCV 2002. 7th European Conference on Computer Vision Proceedings, Copenhagen, Denmark, Part I (Lecture Notes in Computer Science Vol.2350), 2002: 236-250. [6] Wang J, Bo T, Xu Y Q, and Cohen M. Image and video segmentation by anisotropic kernel mean shift[C]. Computer Vision - ECCV 2004. 8th European Conference on Computer Vision, Proceedings (Lecture Notes in Comput. Sci.Vol.3022), Prague, Czech Republic, 2004, Vol.2: 2638-249. [7] 边肇祺, 张学工. 模式识别[M]. 北京: 清华大学出版社, 2000: 180-181. [8] Bradski G R. Computer vision face tracking as a component of a perceptual user interface[C]. Proceedings Fourth IEEE Workshop Applications of Computer Vision, Berlin, Germany, Oct. 1998: 214-219.

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