## 2 Related Works  
### Pedestrian Re-ID Research  
Re-ID was initially studied as a sub-task of multi-camera tracking [4]. In 1997, [PERSON] and [PERSON] et al. [5] first tracked pedestrians between multiple different cameras using appearance features and designed a tracking model that fused color and spatiotemporal features based on the Bayesian theory. [PERSON] et al. [6] and [PERSON] et al. [7] put the luminance transfer function in the same subspace as the spatiotemporal features to address the problem of changing pedestrian colors when tracking with multiple cameras. In 2005, [PERSON] et al. [8] attempted to encode the probabilistic relationship between labels and features from a trajectory using a dynamic Bayesian network. In 2006, [PERSON] et al. [9] used a single visual appearance to match pedestrians recorded by different cameras. Due  
Figure 1: Weak supervision diagram.  
to the rapid development of large datasets and evaluation metrics, metric learning-based approaches were proposed in 2011, and [PERSON] et al. (2010) and [PERSON] et al. (2011) used metric learning to minimize the distance of the optimal matching pairs, which made discriminative similarity metrics a research hotspot ([PERSON] et al., 2012; [PERSON] et al., 2013). In 2012, the AlexNet ([PERSON] et al., 2014) algorithm used on ImageNet received better results, highlighting the great potential of deep learning techniques in the image domain. After 2014, convolutional neural networks (CNNs) started to be introduced into pedestrian re-identification tasks, which allowed the networks to obtain even better features from images. [PERSON] et al. (2015) and [PERSON] et al. (2016) designed Siamese neural networks to learn better pedestrian features from the training images to determine whether a pair of input images belonged to the same pedestrian. Several datasets, such as CUHK03 ([PERSON] et al., 2016) and Market-1501 ([PERSON] et al., 2016), for pedestrian re-recognition training and validation were subsequently made publicly available, making this task a popular research topic in the computer vision community ([PERSON] et al., 2016). In 2016, [PERSON] et al. (2016) proposed the residual learning framework, which made deeper network training possible and allowed for an improvement in the accuracy of deeper networks. In 2017, [PERSON] et al. (2017) proposed a One-Shot method to learn metrics, which achieved a good performance by learning the deep texture of a single instance. The literature ([PERSON] et al., 2016) estimated segment similarity using deep neural networks by dividing long video clips into multiple short video clips and aggregating the top-ranked segment similarities for sequence similarity estimation while maintaining a diverse appearance and the temporal information, which was successful on multiple datasets. In 2020, [PERSON] et al. (2020) proposed momentum contrast for unsupervised visual representation learning, constructing a dynamic dictionary with a queue and moving average encoders. This enables the instant construction of a large and consistent dictionary, which facilitates contrast for unsupervised learning. Many excellent research results have also appeared in the weakly supervised field in recent years, and many of them have been published in top journals. The history of the development of pedestrian re-recognition is shown in Figure 2.  
The complete development process of the pedestrian re-identification task includes a total of five steps: obtaining raw surveillance video data, video extraction image and preprocessing, pedestrian annotation in the image, model building and training, and pedestrian retrieval; most of the current research mainly focuses on the last two steps and, as a result, the existing Re-ID methods can be divided into two main parts: strongly supervised learning and weakly supervised learning ([PERSON] et al., 2016; [PERSON] et al., 2013).