# Person Re-Identification Based on Attention Mechanism and Context Information Fusion
## 1 Introduction  
The core aim of person re-identification (ReID) is to recognize a person captured in different times and/or locations over multiple non-overlapping camera views, considering a large number of candidates. It has attracted widespread attention in recent years because of its importance in many practical applications, such as image retrieval and video surveillance analysis. The result of person ReID query can not only help the police to quickly obtain clues of criminal suspects or find missing children, but also enable businesses to capture the patterns of customer behavior and allocate resource by making more reasonable use of commercial value [1]. The two key steps in person ReID are pedestrian feature extraction and feature similarity matching, respectively [2]. Traditional person ReID methods mainly depend on the features of manual design and measurement learning. [PERSON] et al. [3] propose to overcome the small sample size problem in person ReID distance metric learning, they match people in a discriminative null space of the training data. [PERSON] et al. [4] come up with a method called Local Maximal Occurrence (LOMO), which is helpful for an effective feature representation, and a metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The horizontal occurrence of local features is analyzed by LOMO feature, and the occurrence is maximized to make a stable representation against viewpoint changes. [PERSON] et al. [5] suggest a method of learning mid-level filters from automatically discovered patch clusters for person ReID. The traditional person ReID methods mentioned above often have shortcomings such as insufficient feature representation, low similarity matching accuracy, and slow person ReID process. The advent of deep learning makes person ReID methods based on deep learning become a hot research topic. There are two mainly methods for person ReID based on deep learning, which are feature representation and deep metric learning, respectively. * The method based on feature representation is to perform person ReID tasks by learning a network that can extract significant features of pedestrians. Generally, there are three types of features used for person ReID: global feature, local feature, and auxiliary feature. [PERSON] et al. (2019) regards the pedestrian feature extraction as a classification task, and they train the CNN network by using the pedestrian ID as the label of data. [PERSON] et al. (2017) proposed a person ReID network using only global features in order to avoid the excessive complexity of network structure. [PERSON] et al. (2018) design a new powerful method called AGW (Attention Generalized mean pooling with Weighted triplet loss), which also uses only the global feature of pedestrians, but achieves competitive results. [PERSON] et al. introduced a reverse attention module to handle the problem that the attention mechanism may lead to the loss of important information ([PERSON] et al., 2018). Moreover, some researchers are not satisfied with the information contained in global features, so local feature representation learning is proposed. [PERSON] et al. (2018) apply local feature, and address body misalignment problem by design a two-stream network that uses fine grained semantics. To avoid the problem of missing body parts for pedestrians, [PERSON] et al. propose a Horizontal Pyramid Matching (HPM) approach to make full use of pedestrian body parts ([PERSON] et al., 2018). To further improve the robustness of person ReID model, some auxiliary information is used to train the model. For example, Attribute Attention NetWork (AANet) is a novel model that combines person attributes with attribute attention maps ([PERSON] et al., 2018). In addition to pedestrian attribute information, viewpoint is also a common auxiliary information. Viewpoint-Aware Loss with Angular Regularization (VA-reID) ([PERSON] et al., 2018) takes into account not only the viewpoint, but also the relationship between different viewpoints.