LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle Scenarios - Ling Mei; Mingyu Fu; Bingjie Wang; Lvxiang Jia; Mingyu Yu; Yu Zhang; Lijun Zhang (Remote Sensing)
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.