# Multi-Level Joint Feature Learning for Person Re-Identification
## 1 Introduction  
Public safety incidents often occur in dense crowds. Therefore, a large number of surveillance cameras are installed and applied in various areas of the city. Person re-identification is a key component technology in the field of urban remote sensor monitoring. For a certain target person appearing in a remote sensing surveillance video or remote sensing pedestrian image, the method of person re-identification can accurately and quickly identify this target person in other remote sensing monitoring fields. The goal of person re-identification is to find the same person from the videos or images captured from different cameras [1], as in Figure 1. Recently, deep learning methods achieve great success by designing feature representations [2; 3; 4; 5; 6] or learning robust distance metrics [7; 8; 9; 10]. The pedestrian features extracted by deep learning can be divided into two types: global features and local features. The global features are extracted from the whole picture, which is easy to calculate and intuitive. These features contain the most significant information of a person (such as the color of pedestrian clothes), which is helpful to indicate the identity of different pedestrians [6]. However, some inconspicuous details (such as hats, belts, etc.) are easily ignored by the global features. For example, if two persons are wearing clothes of the same color, and one of them is wearing a hat, it is hard to discriminate the two persons from only the overall appearance. Moreover, when the background is complex, it is difficult for the global features to associate the images of the same person with different backgrounds into one identity, as shown in Figure 2.  
In order to solve the problem of person re-identification, some recent work mainly uses deep learning models to extract local features, using salient local details to match the local features of a queried pedestrian. Local feature information of each body part is extracted by neural network. The similarity between local features is very low, which is more conducive to person re-identification. However, the method of extracting local features may ignore the overall pedestrian information, as shown in Figure 2.  
Representing local information of individuals by locating notable body parts from pedestrian images is also an effective method of person re-identification in recent years [11; 12; 13]. Local features are  
Figure 1: Retrieving the same pedestrian image under different cameras. Figure 2: If the network only extracts local features of (**a**), it cannot be determined that those local features belong to the same person. If the network only extracts global features, complex background content can be detrimental to identifying pedestrians, as shown in (**b**). Horizontal arrows mean we horizontally divided the feature map into six parts and extracted the local features of each part. extracted from different body parts. Each body part contains a small portion of local information from the whole body [14; 15]. In this way, we can learn detailed local features from the divided parts which make the features focus on the local details. The learned local features supplement important details, which can be taken as the complementary of the global features. Therefore, in this paper, the local features and global features are jointly learned for person re-identification. In this paper, we propose a Multi-level Feature Fusion (MFF) model that fuses global features and local features. Moreover, the local features are extracted from different network depths. An MFF model consists of two components: Part-based Multi-level Net (PMN) and Global-Local Branch (GLB). PMN is used to extract local features from different layers of the network. GLB extracts local features and global features at the highest level. The global features and local features are used to perform identity predictions in MFF. We train the MFF model on three classic datasets.