MMCF-Net: A Micro-Motion Context Fusion Framework for Pedestrian Crossing Intention Recognition in Urban Scenes

As advanced vision sensors and edge computing platforms become increasingly integrated into modern vehicles as consumer technologies and electronics, how to accurately recognize pedestrian crossing intentions in urban environments is a noteworthy consideration. Existing research largely relies on skeletal features or trajectory prediction, which demonstrates limited performance in complex scenarios. A Micro-Motion Context Fusion Network (MMCF-Net) is proposed to address these limitations. First, a high-resolution spatiotemporal attention module is constructed that captures sub-pixel-level micro-motion features such as head direction shifts, center of gravity adjustments, and limb pre-activation through deformable convolution and dual attention mechanisms. Second, a hierarchical multi-modal context encoder is designed that adaptively fuses traffic element semantics, vehicle dynamics, and perspective-corrected spatial relationships. Finally, a dynamic weight allocation network is introduced that implements optimal combination of micro-motion features and contextual information based on scene complexity. Experiments demonstrate that MMCF-Net achieves an F1 score of 92.3%, an 7.0% improvement over existing methods like PIE, with enhanced robustness in low-light and occluded scenarios, providing a novel approach to pedestrian safety in complex urban environments.