基于多尺度联合学习的行人重识别

现有的行人重识别方法主要关注于学习行人的局部特征来实现跨摄像机条件下的行人辨识。然而在人体部件存在运动或遮挡、背景干扰等行人数据非完备条件下，会导致行人局部辨识信息丢失概率的增加。针对这个问题，提出了一种多尺度联合学习方法对行人辨识特征进行精细化表达。该方法包含3个分支网络，分别提取行人的粗粒度全局特征、细粒度全局特征和细粒度局部特征。其中粗粒度全局分支通过融合不同层次的语义信息来增强全局特征的丰富性；细粒度全局分支通过联合全部局部特征，在对全局特征进行细粒度描述的同时学习行人局部部件间的相关性；细粒度局部分支则通过遍历局部特征来挖掘行人非显著性的信息以增强局部特征的鲁棒性。为了验证所提方法的有效性，在Market1501、DukeMTMC-ReID和CUHK03三个公开数据集上开展了对比实验，实验结果表明:所提方法取得了最佳性能。 

Multi-scale joint learning for person re-identification

The existing person re-identification approaches mainly focus on learning person's local features to match a specific pedestrian across different cameras. However, in the presence of incomplete conditions of pedestrian data such as motion or occlusion of human body parts, background interference, etc., it leads to an increase in the probability of partial loss of pedestrian recognition information. This paper presents a multi-scale joint learning method to extract the fine-grained person feature. This method consists of three subnets, i.e. coarse-grained global feature extraction subnet, fine-grained global feature extraction subnet, and fine-grained local feature extraction subnet. The coarse-grained global feature extraction subnet enhances the diversity of the global feature by fusing semantic information at different levels. The fine-grained global branching unites all local features to learn the correlation among local components of a pedestrian while describing the global features at a fine-grained level. The fine-grained local feature extraction subnet enhances robustness by traversing local features and finding out pedestrian non-significant information. Comparative experiments have been conducted to evaluate the performance of the proposed method against state-of-the-art methods on Market1501, DukeMTMC-ReID, and CUHK03 person re-identification datasets. The experimental results show that the proposed method has the best performance.