# Multi-Level Fusion Model for Person Re-Identification by Attribute Awareness
## 1 Introduction  
Given a pedestrian image, the purpose of person re-identification is to retrieve images of the pedestrian from cross-camera devices. Person re-identification is an image-retrieval technology designed to compensate for the visual limitations of fixed cameras. It can be combined with pedestrian-detection and tracking technologies for use in fields such as intelligent pedestrian detection and intelligent security [1; 2; 3; 4]. Pedestrians have both rigid and flexible characteristics, due to the differences between camera devices, and their appearance is easily affected by factors such as clothing, posture, weather, and occlusion, making person re-identification one of the most challenging research topics in the field of computer vision. The main idea of traditional image-based person re-recognition is to compare the similarity of two identities. The similarity of different identities is small, and the similarity of the same identity is large. Though the supervised person re-identification problem has labeled information, for most person Re-ID datasets, their boundary boxes are detected by outdated detectors. These limitations make it necessary to improve discriminative performances in person Re-ID. Pedestrian attributes have many commonalities between identities, and there are many differences in pedestrian images with the same identity. As a result, the features extracted by the network often cannot accurately measure the similarity. Therefore, simply relying on the labeled information to determine the pedestrian distance can easily lead to the deviation of the network's attention features. For example, assume three pedestrians have identities \((x_{1},y_{1})\), \((x_{2},y_{2})\), and \((x_{3},y_{3})\), where \(y_{1}\
eq y_{2}\
eq y_{3}\), while \(x_{1}\) and \  
the similarity, both the label and the attribute information will be considered to guide the network to pay attention to the corresponding features. Thus, we can conclude that \(S(x_{1},x_{2})>\max(S(x_{1},x_{3}),S(x_{2},x_{3}))\). Thus, we can conclude that attributes help guide the network to learn the relationship between attributes and features and obtain features with more semantic information. Attributes can also speed up training. Adding attributes can filter out some images that are incompatible with query attributes. Of course, more attributes do not imply better performance. Two classic image-based person re-recognition datasets, Market-1501 and DukeMTMC-reID, have been marked with attribute information. Market-1501 has 30 attributes for each identity, and DukeMTMC-reID has 23. These datasets have many cases of pedestrians with different identities, but whose attributes are very similar. That is, suppose two pedestrians have identities \((x_{1},y_{1},A_{1})\) and \((x_{2},y_{2},A_{2})\), where \(A_{1}=A_{2}\) and \(y_{1}\
eq y_{2}\). It is not possible to directly use attribute labels alone to train the network or to choose discriminative attributes. Therefore, we use identity and attribute labels to jointly train the network. Although current single-modal algorithms have achieved good performance on some standard datasets, there is a long way to go. As we all know, RGB images are sensitive to certain weather conditions such as rain, snow, and fog. However, intelligent monitoring must meet actual needs. The intuitive idea is to mine useful information from other modalities (such as thermal and depth sensors) and fuse it with RGB sensors. Therefore, there has been much work on deep fusion of multi-modal data to improve performance [5]. Scholars have also proposed to introduce attributes to person re-identification tasks. [PERSON] et al. [6] proposed an attribute person recognition (APR) network, a multi-task network that simultaneously learns pedestrian ID embedding and predicts pedestrian attributes. They manually marked attribute labels of two person re-identification datasets and systematically studied the correlation between pedestrian ID and attribute recognition. [PERSON] et al.