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CLC number: TP391

On-line Access: 2014-10-09

Received: 2014-01-05

Revision Accepted: 2014-03-28

Crosschecked: 2014-08-11

Cited: 1

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.10 P.861-877


An advanced integrated framework for moving object tracking

Author(s):  Gwang-Min Choe, Tian-jiang Wang, Fang Liu, Chun-Hwa Choe, Hyo-Son So, Chol-Ung Pak

Affiliation(s):  School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; more

Corresponding email(s):   cca2005@foxmail.com, tjwang@hust.edu.cn, fang.liu@hust.edu.cn

Key Words:  Geogram, Mean shift, Hybrid gradient descent algorithm, Particle filter, Spline resampling, Matrix condition number

Gwang-Min Choe, Tian-jiang Wang, Fang Liu, Chun-Hwa Choe, Hyo-Son So, Chol-Ung Pak. An advanced integrated framework for moving object tracking[J]. Journal of Zhejiang University Science C, 2014, 15(10): 861-877.

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publisher="Zhejiang University Press & Springer",

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%DOI 10.1631/jzus.C1400006

T1 - An advanced integrated framework for moving object tracking
A1 - Gwang-Min Choe
A1 - Tian-jiang Wang
A1 - Fang Liu
A1 - Chun-Hwa Choe
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A1 - Chol-Ung Pak
J0 - Journal of Zhejiang University Science C
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EP - 877
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1400006

This paper first introduces the concept of a geogram that captures richer features to represent the objects. The spatiogram contains some moments upon the coordinates of the pixels corresponding to each bin, while the geogram contains information about the perimeter of grouped regions in addition to features in the spatiogram. Then we consider that a convergence process of mean shift is divided into obvious dynamic and steady states, and introduce a hybrid technique of feature description, to control the convergence process. Also, we propose a spline resampling to control the balance between computational cost and accuracy of particle filtering. Finally, we propose a boosting-refining approach, which is boosting the particles positioned in the ill-posed condition instead of eliminating the ill-posed particles, to refine the particles. It enables the estimation of the object state to obtain high accuracy. Experimental results show that our approach has promising discriminative capability in comparison with the state-of-the-art approaches.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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