Semantic Part Constraint for Person Re-identification
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摘要: 为减轻行人图片中的背景干扰，使网络着重于行人前景并且提高前景中人体部位的利用率，该文提出引入语义部位约束(SPC)的行人再识别网络。在训练阶段，首先将行人图片同时输入主干网络和语义部位分割网络，分别得到行人特征图和部位分割图；然后，将部位分割图与行人特征图融合，得到语义部位特征；接着，对行人特征图进行池化得到全局特征；最后，同时使用身份约束和语义部位约束训练网络。在测试阶段，由于语义部位约束使得全局特征拥有部位信息，因此测试时仅使用主干网络提取行人的全局信息即可。在大规模公开数据集上的实验结果表明，语义部位约束能有效使得网络提高辨别行人身份的能力并且缩减推断网络的计算花费。与现有方法比较，该文网络能更好地抵抗背景干扰，提高行人再识别性能。Abstract: In order to alleviate the background clutter in pedestrian images, and make the network focus on pedestrian foreground to improve the utilization of human body parts in the foreground. In this paper, a person re-identification network is proposed that introduces Semantic Part Constraint(SPC). Firstly, the pedestrian image is input into the backbone network and the semantic part segmentation network at the same time, and the pedestrian feature map and the part segmentation label are obtained respectively. Secondly, the part segmentation label and the pedestrian feature maps are merged to obtain the semantic part feature. Thirdly, the pedestrian feature map is obtained and the global average pooling is used to gain global features. Finally, the network is trained using both identity constraint and semantic part constraint. Since the semantic part constraint makes the global features obtain the part information, only the backbone network can be used to extract the features of the pedestrian during the test. Experiments on large-scale datasets show that semantic part constraints can effectively make the network improve the ability to identify pedestrians and reduce the computational cost of inferring networks. Compared with the state of art, the proposed network can better resist background clutter and improve person re-identification performance.
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表 1 在Market-1501数据集上的对比实验(%)
实验编号 行人特征 网络约束 Rank-1 Rank-5 Rank-10 mAP 1 ${{{f}}_{\rm{g}}}$ ${L_{{\rm{id}}}}$ 92.0 96.9 98.2 80.4 2 ${{{C}}_f}$ ${L_{{\rm{id}}}}$ 92.7 97.5 98.6 80.6 3 ${{{f}}_{\rm{g}}}$ ${L_{{\rm{id}}}} + {L_{{\rm{sp}}}}$ 93.6 97.6 98.7 83.6 
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表 3 不同方法在两个数据集上的性能比较(%)
方法 Market-1501 DukeMTMC-reID Rank-1 mAP Rank-1 mAP VIM[11] 79.5 59.9 68.9 49.3 SVDNet[12] 82.3 62.1 76.7 56.8 APR[3] 84.3 64.7 70.7 51.2 FMN[13] 86.0 67.1 74.5 56.9 PSE[14] 87.7 69.0 79.8 62.0 PN-GAN[15] 89.4 72.6 73.6 53.2 CamStyle[16] 89.5 71.6 78.3 57.6 HA-CNN[17] 91.2 75.7 80.5 63.8 Part-Aligned[4] 91.7 79.6 84.4 69.3 SPReID[5] 92.5 81.3 84.4 71.0 AHR[18] 93.1 76.2 81.7 65.9 本文方法 93.6 83.6 85.4 71.3
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