Pedestrian Dynamics Modeling and Social Force Analysis Based on Object Detection

Daoyu Zhu^*

• Xinyang Normal University, Xinyang, China

Object detection is a fundamental component of modern computational applications, playing a crucial role in pedestrian analysis, autonomous navigation, and crowd monitoring. Despite its widespread utility, pedestrian-oriented object detection faces significant challenges, including dynamic crowd behaviors, occlusions, multi-scale variability, and complex urban environments, which hinder the accuracy and robustness of existing models. To address these challenges, we propose a novel framework that integrates the Information-Geometric Variational Inference Framework (IGVIF) with the Adaptive Exploration-Exploitation Trade-off Strategy (AEETS), specifically tailored for pedestrian dynamics. IGVIF formulates pedestrian detection as a probabilistic inference problem, leveraging principles from information geometry to efficiently explore high-dimensional parameter spaces. By incorporating techniques such as Riemannian optimization and multi-scale parameterization, IGVIF effectively captures the hierarchical and multi-modal structures inherent in pedestrian movement patterns. Complementing IGVIF, AEETS dynamically balances global exploration with local refinement using entropy-based metrics and feedback-driven adjustments, allowing the system to adaptively optimize complex loss landscapes with greater precision in pedestrian scenarios. Together, these components create a robust and adaptive framework that overcomes traditional limitations by efficiently handling largescale pedestrian variability and densely populated environments. Experimental evaluations across multiple real-world pedestrian datasets demonstrate the superiority of our physicsinspired approach, achieving state-of-the-art performance in pedestrian detection and movement analysis. This work highlights the transformative potential of interdisciplinary strategies in advancing pedestrian-aware object detection, bridging computational physics with deep learning methodologies to enhance urban mobility and crowd safety.