# Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery
## 1 Introduction  
Pedestrian re-identification (ReID) is a crucial technology used in video surveillance, intelligent transportation, and security systems [1]. Its main objective is to accurately identify and match the same pedestrian across various cameras and perspectives, enabling numerous practical applications. Unlike Pedestrian Recognition, which categorizes pedestrians from different cameras and viewpoints, pedestrian ReID focuses on distinguishing individuals within the same category. This requires advanced feature extraction and model development, as ReID must not only differentiate between various pedestrians but also accurately match the same individuals in large-scale datasets. To address the challenges of pedestrian ReID in large-scale and complex environments, researchers have explored more fine-grained and efficient object identification methods. Specific Object Identification (SOI) has emerged as a significant research direction. Various approaches have been proposed to achieve efficient SOI, including discriminative feature extraction using deep learning networks to capture unique information that distinguishesindividuals in pedestrian images; fuzzy clustering and classification methods for handling complex and uncertain data, especially when different individuals belong to the same pedestrian class; and hierarchical clustering methods for more detailed pedestrian classification through multilevel clustering. These methods not only extract unique local features in pedestrian images but also enhance individual recognition through refined classification schemes. However, to address the challenges posed by unbalanced data distributions and complex feature spaces, reference [2] introduced an enhanced gravitational classifier based on the Geometrical Divide method. This approach addresses the inefficiencies of the traditional \(1\div 1\) composite base method in managing overlapping categories by geometrically partitioning class data particles. Using the Moons and Circles datasets, the study experimentally validated the Geometrical Divide method, employing multiple data particle quality determination algorithms alongside k-fold cross-validation. The results demonstrate that the Geometrical Divide method outperforms traditional approaches in classifying these datasets. [PERSON] et al. [3] further explored the Geometrical Divide (GD) method and its improved variant, the Unequal Geometrical Divide (UGD), within a gravitational classification framework. The GD method alleviates inter-class overlap by determining dividing lines through the center of mass and geometric center calculations. The UGD method builds upon this by enhancing the handling of minority class samples. Experimental evaluations confirmed the superior performance of these methods on various unbalanced datasets. While originally designed for general classification tasks, the underlying theoretical framework provides valuable insights for feature optimization and pedestrian re-identification in complex environments. In recent years, UAVs have shown significant advantages in large-scale surveillance due to their flexibility and wide-area coverage capabilities [4]. UAVs can adjust their flight height and path to capture multi-angle aerial images, making them extensively studied for target detection. Additionally, multi-sensor data fusion technology offers a novel approach for UAV target detection, significantly enhancing both the accuracy and robustness of detections. [PERSON] et al. [5] introduced a two-layer fusion strategy. First, they compared visual detection results with radar detection projections in the RGB image plane to eliminate radar clutter. Next, they fused radar and visual information using extended Kalman filtering (EKF) to accurately determine the target's position and velocity. [PERSON] et al. [6] significantly enhanced detection accuracy in low-visibility environments by superimposing infrared images onto RGB images, creating multi-channel input data. [PERSON] et al.