# Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery
## 2 Related Work  
### Conventional Pedestrian Re-Identification  
Early ReID methods predominantly relied on global features [12], neglecting the capture of local information and resulting in relatively low identification accuracy. To overcome this limitation, researchers have progressively incorporated local feature learning techniques [13], enabling the capture more detailed characteristics of pedestrians. However, this approach introduces new challenges, including redundancy and instability in local information. To address these issues and further enhance the model's identification capabilities, researchers have integrated multi-scale feature fusion [14] and attention mechanisms [15]. These methods improve the model's ability to effectively capture and interpret features across different scales and granularities. For example, reference [16] enhances the capture of pedestrian contours and local details by introducing edge information. Reference [17] implements an attention pooling mechanism that allows the model to focus on salient regions in pedestrian images, thereby effectively extracting discriminative features and mitigating the effects of scale, pose, and occlusion. Additionally, to optimize the expressiveness of the feature space, various loss functions have been designed to improve identification accuracy. Specifically, reference [18] proposes a novel loss function, Marginal Cosine Softmax Loss (MCSL), which enhances the performance of pedestrian ReID models in metric learning. With the expansion of datasets and the increase in annotation costs, unsupervised learning has gradually become a research hotspot for pedestrian re-identification [19; 20]. SOI effectively identifies and differentiates similar pedestrians by focusing on feature information in pedestrian images. It combines clustering and optimization algorithms to deeply mine the intrinsic structure and patterns of the data, thereby enhancing the model's adaptability. The attention-driven framework (AFC) proposed in reference [21] integrates attention mechanisms with clustering optimization techniques. This combination further improves the model's identification ability, especially when relying on clustering methods and pseudo-labeling optimization, effectively addressing the uncertainty problem. The Enhanced Feature Representation and Robust Clustering (EFRRC) method introduced in reference [22] enhances feature representation among human body parts by incorporating a relational network. It extracts global features from images through the Global Comparison Pooling (GCP) module, resolving issues related to data inhomogeneity and clustering, thereby improving identification accuracy in complex scenarios. [PERSON] et al. [23] proposed a bottom-up clustering method aimed at making clustering results more consistent with real-world distributions by jointly optimizing the relationship between the model and individual samples. Conversely, [PERSON] et al. [24] introduced a hierarchical clustering method that employs a hard-batch ternary loss. This loss function is crucial for mining similarities between samples through hierarchical clustering and mitigating the impact of complex samples on clustering results, thereby improving label quality. Graph convolutional networks (GCNs) [25] have also been applied to pedestrian re-identification tasks to better process structural information in images and enhance identification robustness. Additionally, reference [26] proposes a new architecture that combines attribute features by mapping manually labeled pedestrian features to word vectors and integrating them with body part features. This approach enhances feature representation in occlusion situations. To address these limitations, Transformer-based models have been proposed for pedestrian re-identification [27]. These models offer enhanced capabilities in capturing long-range dependencies. They also improve the understanding of global contextual information. An exemplary application is TransReID [28].