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Front. Inform. Technol. Electron. Eng.  2014, Vol. 15 Issue (10): 861-877    DOI: 10.1631/jzus.C1400006
    
An advanced integrated framework for moving object tracking
Gwang-Min Choe, Tian-jiang Wang, Fang Liu, Chun-Hwa Choe, Hyo-Son So, Chol-Ung Pak
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; School of Computer Science and Technology, Kim Il Sung University, Pyongyang, DPR of Korea; School of Wireless Engineering, Huichon Institute of Technology, Huichon, DPR of Korea
An advanced integrated framework for moving object tracking
Gwang-Min Choe, Tian-jiang Wang, Fang Liu, Chun-Hwa Choe, Hyo-Son So, Chol-Ung Pak
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; School of Computer Science and Technology, Kim Il Sung University, Pyongyang, DPR of Korea; School of Wireless Engineering, Huichon Institute of Technology, Huichon, DPR of Korea
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摘要： 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.
关键词： Geogram;  Mean shift;  Hybrid gradient descent algorithm;  Particle filter;  Spline resampling;  Matrix condition number    
Abstract: 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.
Key words: Geogram    Mean shift    Hybrid gradient descent algorithm    Particle filter    Spline resampling    Matrix condition number
收稿日期: 2014-01-05 出版日期: 2014-10-09
CLC:  TP391  
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Gwang-Min Choe
Tian-jiang Wang
Fang Liu
Chun-Hwa Choe
Hyo-Son So
Chol-Ung Pak

引用本文:

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. Front. Inform. Technol. Electron. Eng., 2014, 15(10): 861-877.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1400006    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2014/V15/I10/861

[1] Yan-xia Jin, Kai Zhang, James T. Kwok, Han-chang Zhou. Fast and accurate kernel density approximation using a divide-and-conquer approach[J]. Front. Inform. Technol. Electron. Eng., 2010, 11(9): 677-689.