Person re-identification (ReID) plays a crucial role in advancing UAV-based surveillance applications, enabling robust tracking and event analysis. However, existing methods in UAV scenarios primarily focus on individual pedestrians, requiring cumbersome annotation efforts and lacking seamless integration with ground-based surveillance systems. These limitations hinder the broader development of UAV-based monitoring. To address these challenges, this paper proposes an Unsupervised Aerial-Ground Re-identification from Pedestrian to Group (UAGRPG) framework. Specifically, we introduce a neighbor-aware collaborative learning (NCL) and gradual graph matching (GGC) strategy to uncover the implicit associations between cross-modality groups in an unsupervised manner. Furthermore, we develop a collaborative cross-modality association learning (CCAL) module to bridge feature disparities and achieve soft alignment across modalities. To quantify the optimal group similarity between aerial and ground domains, we design a minimum pedestrian distance transformation strategy. Additionally, we introduce a new AG-GReID dataset, and extensive experiments demonstrate that our approach achieves state-of-the-art performance on both pedestrian and group re-identification tasks in aerial-ground scenarios, validating its effectiveness in integrating ground and UAV-based surveillance.

The integration of pedestrian movement analysis with Unmanned Aerial Vehicle (UAV)-based remote sensing enables comprehensive monitoring and a deeper understanding of human dynamics within urban environments, thereby facilitating the optimization of urban planning and public safety strategies. However, human behavior inherently involves uncertainty, particularly in the prediction of pedestrian trajectories. A major challenge lies in modeling the multimodal nature of these trajectories, including varying paths and targets. Current methods often lack a theoretical framework capable of fully addressing the multimodal uncertainty inherent in trajectory predictions. To tackle this, we propose a novel approach that models uncertainty from two distinct perspectives: (1) the behavioral factor, which reflects historical motion patterns of pedestrians, and (2) the stochastic factor, which accounts for the inherent randomness in future trajectories. To this end, we introduce a global framework named LSN-GTDA, which consists of a pair of symmetrical U-Net networks. This framework symmetrically distributes the semantic segmentation and trajectory prediction modules, enhancing the overall functionality of the network. Additionally, we propose a novel thermal diffusion process, based on signal and system theory, which manages uncertainty by utilizing the full response and providing interpretability to the network. Experimental results demonstrate that the LSN-GTDA method outperforms state-of-the-art approaches on benchmark datasets such as SDD and ETH-UCY, validating its effectiveness in addressing the multimodal uncertainty of pedestrian trajectory prediction.