基于注意力机制和姿态识别的行人再识别

Person re-identification based on attention mechanism and gesture recognition

在解决行人再识别技术中的姿态变化、遮挡、背景等问题时,为了提高遮挡下的行人再识别性能,提出一种基于注意力机制和姿态识别的行人再识别方法。采用全局注意网络和姿态识别网络分别提取行人图像的全局特征、关节点位置热力图和对应的置信度,通过计算得到行人13个关节点和融合所有关节点的局部特征,对全局特征和14个局部特征分别进行行人分类训练,利用多任务学习多个损失共同监督网络的优化。测试时,将关键点特征和全局特征融合后,计算行人的距离排序。在Market1501和DukeMTMC-reID数据集上测试的Rank-1/mAP指标分别达到了85.1%/75.6%和64.3%/55.3%。结果表明,所设计方法具备抗姿态变化、遮挡和背景的能力,同时具有较高的识别能力和识别精度。

In the person re-identification of the posture change,occlusion,background and other issues,in or⁃der to improve the performance of person re-identification under occlusion,a person re-recognition method based on the combination of attention mechanism and gesture recognition was proposed.The global attention network and the posture recognition network were used to extract the global features,the thermal map of the joint position and the corresponding confidence,respectively.The local features of pedestrians at 13 joint points and fusion of all joints were obtained by calculation.Pedestrian classification training was conducted for global features and 14 local features respectively,and multi-task learning was used to learn multiple losses to jointly supervise network optimization.The Rank-1/mAP values tested on the two large-scale public datasets Market1501 and DukeMTMC-reID reached 85.1%/75.6%and 64.3%/55.3%,respectively.It was shown that the designed method with high recognition accuracy had excellent ability resist to posture changes,occlusion and background.