[1] An Guocheng, Gao Jianpo, Wu Zhenyang, et al. Mean shift algorithm based on fusion model for head tracking [J]. Journal of Southeast University (English Edition), 2009, 25 (3): 299-302. [doi:10.3969/j.issn.1003-7985.2009.03.003]

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Mean shift algorithm based on fusion model for head tracking()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

25

Issue:

2009 3

Page:

299-302

Research Field:

Computer Science and Engineering

Publishing date:

2009-09-30

Info

Title:

Mean shift algorithm based on fusion model for head tracking

Author(s):

An Guocheng¹;  2;  Gao Jianpo¹;  Wu Zhenyang¹

¹School of Information Science and Engineering, Southeast University, Nanjing 210096, China
²Intelligence Engineering Lab, Institute of Software Chinese Academy of Sciences, Beijing 100190, China

Keywords:

mean shift;  head tracking;  kernel density estimate;  fusion model

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2009.03.003

Abstract:

To solve the mismatch between the candidate model and the reference model caused by the time change of the tracked head, a novel mean shift algorithm based on a fusion model is provided.A fusion model is employed to describe the tracked head by sampling the models of the fore-head and the back-head under different situations.Thus the fusion head reference model is represented by the color distribution estimated from both the fore-head and the back-head.The proposed tracking system is efficient and it is easy to realize the goal of continual tracking of the head by using the fusion model.The results show that the new tracker is robust up to a 360° rotation of the head on a cluttered background and the tracking precision is improved.

References:

[1] Fukunaga K, Hostetler L.The estimation of the gradient of a density function, with applications in pattern recognition[J].IEEE Transactions on Information Theory, 1975, 21(1):32-40.
[2] Cheng Yizong.Mean shift, mode seeking, and clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8):790-799.
[3] An Guocheng, Chen Jianjun, Wu Zhenyang. A fast external force model for snake-based image segmentation[C]//IEEE International Conference on Signal Processing.Beijing, China, 2008:1128-1131.
[4] Zhou Huiyu, Yuan Yuan, Shi Chunmei. Object tracking using SIFT features and mean shift[J].Computer Vision and Image Understanding, 2009, 113(3):345-352.
[5] Peng Ningsong, Yang Jie, Liu Zhi.Mean shift blob tracking with kernel histogram filtering and hypothesis testing[J].Pattern Recognition Letters, 2005, 26(5):605-614.
[6] Shan Caifeng, Tan Tieniu, Wei Yucheng.Real-time hand tracking using a mean shift embedded particle filter[J].Pattern Recognition, 2007, 40(7):1958-1970.
[7] Yang Changjiang, Duraiswami R, Davis L.Efficient mean-shift tracking via a new similarity measure[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, 2005:176-183.
[8] Hu Jwu-Sheng, Juan Chung-Wei, Wang Jyun-Ji.A spatial-color mean-shift object tracking algorithm with scale and orientation estimation[J].Pattern Recognition Letters, 2008, 29(16):2165-2173.

Memo

Memo:

Biographies: An Guocheng(1979—), male, doctor;Wu Zhenyang(corresponding author), male, professor, zhenyang@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.60672094, 60673188, U0735004), the National High Technology Research and Development Program of China(863 Program)(No.2008AA01Z303), the National Basic Research Program of China(973 Program)(No.2009CB320804).
Citation: An Guocheng, Gao Jianpo, Wu Zhenyang.Mean shift algorithm based on fusion model for head tracking[J].Journal of Southeast University(English Edition), 2009, 25(3):299-302.

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