# Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification
###### Abstract  
Person re-identification is a typical computer vision problem which aims at matching pedestrians across disjoint camera views. It is challenging due to the misalignment of body parts caused by pose variations, background clutter, detection errors, camera point of view variation, different accessories and occlusion. In this paper, we propose a person re-identification network which fuses global and local features, to deal with part misalignment problem. The network is a four-branch convolutional neural network (CNN) which learns global person appearance and local features of three human body parts respectively. Local patches, including the head, torso and lower body, are segmented by using a U_Net semantic segmentation CNN architecture. All four feature maps are then concatenated and fused to represent a person image. We propose a DropParts method to solve the parts missing problem, with which the local features are weighed according to the number of parts found by semantic segmentation. Since three body parts are well aligned, the approach significantly improves person re-identification. Experiments on the standard benchmark datasets, such as Market1501, CUHK03 and DukeMTMC-reID datasets, show the effectiveness of our proposed pipeline.  
Keywords:person re-identification; representation learning; parts alignment; occlusion handling +
Footnote †: journal:  
row, poses variation, background clutter, detection errors, camera point of view variation occlusion also occur. Part misalignment occurs frequently and degrades the performance of person re-identification.  
To solve this problem, many scholars focus on person re-identification based on part alignment recently. Some methods divide the person image into many stripes or grids to reduce the effects of part misalignment [7; 10]. The division of grids or strips is predefined and heuristic, which can't locate the parts precisely. Pose-based methods [5; 11] employ a pose estimation model to infer corresponding bounding boxes. However, parts missing is ineluctable; it causes the convolutional neural network to not work properly.  
This paper focuses on the problem of body part misalignment. It proposes a human parts semantic segmentation aware representation learning method for person re-identification. We employ semantic segmentation network to infer corresponding bounding boxes, and propose a DropParts method to solve the part missing problem. Experiments on the standard benchmark datasets show the effectiveness of our proposed pipeline. The contributions of this paper are as follows:  
(1) We design a four-branch convolutional neural network to deal with parts misalignment problem. The four-branch CNN network learns a person's appearance features globally and using the features of three local body parts. The bounding boxes of three body parts are inferred from human parts semantic segmentation results, which are learned with a popular U_Net [12] semantic segmentation network.  
(2) We propose a DropParts method to solve the part missing problem, with which the local features are weighed due to the appearance vector and fused with global feature. The DropParts method makes the four-branch convolutional neural network work properly when part missing occurs. On the other hand, it improves the performance of person re-identification.