## 1 Introduction  
Person re-identification (Re-ID) is a significant task in computer vision. It aims to address the problem of retrieving and matching the same individual across different camera views in a cross-camera environment, i.e., retrieving a given person from a large set of images. This technology achieves tracking and localization of specific pedestrian targets within large-scale video surveillance networks by extracting and comparing their features captured by various surveillance cameras. Re-ID holds crucial theoretical value and offers extensive practical applications in domains such as public safety, intelligent transportation, and smart cities. Traditional pedestrian re-identification methods primarily rely on manually designed feature representations and similarity measurement techniques. These methods typically start by extracting low-level or mid-level features from pedestrian images, such as color histograms, texture features, and local descriptors (e.g., SIFT, HOG). Subsequently, they utilize metrics like Euclidean distance, Mahalanobis distance, or cosine similarity for feature matching [1; 2; 3]. However, these approaches exhibit significant limitations: (1) Limited Feature Representation: Manually designed features often struggle to comprehensively and effectively capture the complexity and diversity of pedestrian characteristics. This limitation becomes especially pronounced in complex scenarios involving variations in lighting, occlusion, and pose changes. (2) Shallow Feature-Based Distance Metrics: Distance metrics based on shallow features may fail to accurately reflect the deep semantic similarity among pedestrians. Consequently, this can lead to reduced recognition performance across different viewpoints and scenes. The introduction of deep learning into the field of pedestrian re-identification undoubtedly provides novel perspectives and powerful tools for addressing this complex problem. Leveraging deep neural networks, particularly convolutional neural networks (CNNs), allows us to progressively extract high-level abstract features representing pedestrian identities from raw pixel-level data. This overcomes the limitations of traditional methods in terms of feature expressiveness. Currently, the steps involved in deep learning-based pedestrian re-identification are as follows: during the training phase, a deep CNN model is employed for end-to-end learning on pedestrian images. This model directly extracts discriminative feature representations from input images. Network weights are adjusted based on label feedback, and after multiple iterations, a mature network is formed. Widely used pre-trained models such as ResNet [4], VGG [5], and PCB [6] serve as the foundation, with fine-tuning to adapt them specifically to the task of pedestrian re-identification. In the evaluation phase, the trained network is utilized to extract features from both probe and gallery images. The goal is to match these features and select the top-n images with the highest similarity as the target pedestrian image. Researchers have proposed various methods to enhance network recognition accuracy from different perspectives. To improve the recognition accuracy of the network, researchers have proposed a variety of methods to improve network performance from different perspectives, such as data augmentation [7], feature fusion [8; 9], feature sorting [10; 11], and so on, and have achieved impressive results. While pedestrian re-identification (Re-ID) based on convolutional neural networks (CNNs) has achieved commendable performance, it still faces numerous challenges in real-world scenarios. These challenges arise due to factors such as insufficient data volume, variations in lighting conditions, occlusions, diverse poses, varying resolutions, and background interference. Figure 1 illustrates some typical examples of these challenges. In these typical challenges, background interference is the most common phenomenon.