姿态驱动的局部特征对齐的行人重识别
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黑龙江省自然科学基金(LH2020F003); 黑龙江省高等教育教学改革项目(SJGY20210109)


Pose-driven Person Re-identification with Local Feature Alignment
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    摘要:

    针对行人重识别研究中的遮挡问题, 本文提出了一种姿态驱动的局部特征对齐的行人重识别方法. 网络主要包括姿态编码器和行人部件对齐模块. 其中, 姿态编码器通过重构姿态估计热力图抑制遮挡区域骨骼关键点置信度, 引导网络提取行人可见部位的特征. 行人部件对齐模块依据姿态编码器输出的关键点置信图, 提取行人局部特征进行特征对齐, 降低非行人特征的干扰. 在遮挡、半身数据集上的仿真实验表明, 该方法获得了优于其他对比网络的结果.

    Abstract:

    To address the occlusion problem in person re-identification, this study presents a person re-identification method based on pose-driven local feature alignment. The network mainly consists of a pose encoder (PE) and a human part alignment module (HPAM). Specifically, the PE restrains the confidence of the key points on the bones in obscured areas by reconstructing the pose estimation heatmap to guide the network to extract the features of the person’s visible parts. The HPAM extracts the person’s local features according to the confidence map of the key points output by the PE for feature alignment, which further reduces the interference of non-person features. The simulation and experiments on occlusion datasets and half-body datasets show that the proposed method delivers better results than those produced by other networks under comparison.

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王琦,刘志刚,王淼,赵宜珺.姿态驱动的局部特征对齐的行人重识别.计算机系统应用,2023,32(4):268-273

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  • 收稿日期:2022-08-30
  • 最后修改日期:2022-09-27
  • 在线发布日期: 2023-02-10
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