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Robust Object Tracking via Improved Mean-Shift Model

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成果类型:
期刊论文
作者:
Wang, Liqun;Shi, Xuenan;Han, Sunyi;Jinchi
通讯作者:
Wang, Liqun(guoshuqiang@gmail.com)
作者机构:
[Han, Sunyi; Wang, Liqun; Shi, Xuenan] School of Information Engineering, Northeast Electric Power University Jilin, Jilin, 132012, China
[Jinchi] School of Computer Science and Technology, University of South China, China
语种:
英文
期刊:
Lecture Notes in Electrical Engineering
ISSN:
1876-1100
年:
2018
卷:
425
页码:
86-93
机构署名:
本校为其他机构
院系归属:
计算机科学与技术学院
摘要:
In this paper we propose a robust object tracking algorithm using a improved Mean-Shift model. As the traditional Mean-Shift algorithm for object tracking uses a single histogram. Because the traditional Mean-Shift lacks spatial distribution information, so it is difficult to track non-rigid object especially. With a focus on this problem, an improved Mean-Shift algorithm based on the shape feature and color of the target is presented. The results show that the algorithm can track the moving vehicles in real time, and it has a preferable adaptability and robustness to the irregular motion and ...

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