Automatic parts selection method based on multi-feature fusion

被引：0

作者：

Chen H. ^{[1
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Luo H. ^{[1
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,4
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Hui B. ^{[1
,2
,4
,5
]}

Chang Z. ^{[1
,2
,4
,5
]}

机构：

[1] Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang

[2] Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang

[3] University of Chinese Academy of Sciences, Beijing

[4] Key Laboratory of Opto-electronic Information Processing, Chinese Academy of Sciences, Shenyang

[5] The Key Lab of Image Understanding and Computer Vision, Shenyang

来源：

Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2021年 / 50卷 / 08期

关键词：

Automatic parts selection; Computer vision; Deformable parts model; Multi-feature fusion; Target tracking;

D O I：

10.3788/IRLA20200407

中图分类号：

学科分类号：

摘要：

Deformable parts model target tracking methods becomes an active research due to its effectiveness in tackling partial occlusion and deformation issues of targets. When partial occlusion or deformation occurs, deformable parts model trackers could achieve accurate tracking via the uncovered reliable parts. Most of the part-based trackers initialize the number and size of parts manually. In practical tracking systems, it is difficult to provide the interaction to select parts manually. Meanwhile, manual parts selection method might be affected by subjective factors. Aimed at the problems mentioned, automatic parts selection method based on multi-feature fusion was proposed. Firstly, the saliency measure based on human visual attention mechanism was applied to describe the salient region of target template. Secondly, edge direction dispersion was employed to describe the richness of texture details. After obtaining the joint suitable-matching confidence map, the number and size of parts were adaptively selected according to the pixel area and aspect ratio of the target. Finally, the parts were selected according to the joint suitable-matching confidence. Experimental results show that the proposed method can achieve more tracking precision compared with the current deformable parts model target tracking algorithm which selects the parts manually. © 2021, Editorial Board of Journal of Infrared and Laser Engineering. All right reserved.

引用

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