【摘要】 将全局特征与局部特征相结合是提高行人再识别(re-identification)任务识别能力的重要解决方案。以往主要借助姿态估计等外部信息来定位有相应语义的区域,从而挖掘局部信息,这种方法大多是非端到端的,训练过程复杂且缺乏鲁棒性。针对该问题,文中提出了一种能有效挖掘局部信息并且能结合全局信息与局部信息进行端到端特征学习的方法,即多方向分区网络(Multi-orientation Partitioned Network, MOPN),该网络有3个分支,一个用于提取全局特征,两个用于提取局部特征。该算法不依靠外部信息,而是在不同的局部分支分别将图像按水平方向和竖直方向切分为若干横条纹和竖条纹,从而得到不同的局部特征表示。在Market-1501、DukeMTMC-reID、CUHK03和跨模态素描数据集SketchRe-ID上的综合实验表明,该算法的整体性能优于其他对比算法,具备有效性和鲁棒性。更多还原

【Abstract】 Combining global features with local features is an important solution to improve discriminative performances in person re-identification(Re-ID) task.In the past, external information was used to locate regions with corresponding semantics, thus mining local information.Most of these methods are not end-to-end, so the training process is complex.To solve this problem, a multi-orientation partitioned network(MOPN) is proposed, which can effectively mine local information and combine global information with local information for end-to-end feature learning.The network has three branches: one for extracting global feature and two for mining local information.Without relying on external information, the algorithm divides pedestrians’ images into hori-zontal and vertical stripes in different local branches respectively, so as to obtain different local feature representations.Plenty of experiments conducted on Market-1501,DukeMTMC-reID,CUHK03 and cross-modal dataset SketchRe-ID show that the proposed method has better overall performance than other comparison algorithms, and is effective and robust.更多还原