| 采用多特征融合的子块自动提取方法 |
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| 引用本文： | 陈宏宇,罗海波,惠斌,常铮.采用多特征融合的子块自动提取方法[J].红外与激光工程,2021,50(8):20200407-1-20200407-9. |
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| 作者姓名： | 陈宏宇 罗海波 惠斌 常铮 |
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| 作者单位： | 1.中国科学院沈阳自动化研究所，辽宁 沈阳 110016 |
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| 基金项目： | 军科委基础加强计划技术领域重点基金(2020-JCJQ-JJ-503) |
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| 摘 要： | 基于可变形模型的目标跟踪算法因其能够处理目标部分遮挡及形变问题成为目标跟踪领域的研究热点。当目标发生形变或部分遮挡时，可变形模型跟踪器可利用未被遮挡的子块继续完成跟踪。现有基于子块的目标跟踪算法均为手动选取子块的个数和尺寸，但在实际应用中，很难为子块的选取提供人机交互的机会，且手动选取子块易受主观因素影响。针对上述情况，提出了一种采用多特征融合的子块自动提取方法，该方法首先采用基于人眼视觉注意机制对目标模板的显著性区域进行度量；其次，利用边缘方向离散度对目标的纹理丰富度进行度量；然后，融合上述特征获得联合适配性置信度，并根据目标的面积和宽高比自适应确定子块的个数和尺寸；最后，根据联合适配性置信度提取目标子块。实验结果表明，与现有手动选取子块的可变形模型目标跟踪方法相比，采用所提方法自动提取的子块可获得更高的跟踪精度。
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| 关 键 词： | 计算机视觉 目标跟踪 可变形模型 多特征融合 子块自动选取 |
| 收稿时间： | 2020-12-23 |
Automatic parts selection method based on multi-feature fusion |
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| Affiliation: | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China3.University of Chinese Academy of Sciences, Beijing 100049, China4.Key Laboratory of Opto-electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China5.The Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China |
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| Abstract: | 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. |
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