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

doi:10.1631/jzus.C1400006

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An advanced integrated framework for moving object tracking

Electrical & Electronic Engineering | Updated：2022-05-19

- An advanced integrated framework for moving object tracking
- An advanced integrated framework for moving object tracking
- Frontiers of Information Technology & Electronic Engineering Vol. 15, Issue 10, Pages: 861-877(2014)
- Affiliations：
  
  1.School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
  2.School of Computer Science and Technology, Kim Il Sung University, Pyongyang, DPR of Korea
  3.School of Wireless Engineering, Huichon Institute of Technology, Huichon, DPR of Korea
- Author bio：
  
  [ "Gwang-Min CHOE, cca2005@foxmail.com" ]
  Tian-jiang WANG, E-mail: tjwang@hust.edu.cn
  [ "Fang LIU, fang.liu@hust.edu.cn" ]
- Funds：
- DOI：10.1631/jzus.C1400006
  CLC： TP391
- Received：05 February 2014，
  
  Revised：2014-;8-11，
  
  Published：2014-10
- Accepted：
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Gwang-Min CHOE, Tian-jiang WANG, Fang LIU, et al. An advanced integrated framework for moving object tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(10): 861-877.
DOI：

Gwang-Min CHOE, Tian-jiang WANG, Fang LIU, et al. An advanced integrated framework for moving object tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(10): 861-877. DOI： 10.1631/jzus.C1400006.

摘要

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.

关键词

Keywords

references

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