Depth-aware Pedestrian Situation Classification for Enhanced Safety Monitoring in Smart Cities
Dae Hoe Kim, Jinyoung Moon · 2025
As urban environments grow increasingly complex, accurate classification of pedestrian situations becomes vital for public safety in smart cities. However, few approaches address pedestrian situation assessment beyond general-purpose detection and tracking. This paper presents a novel pedestrian situation classification method that employs depth information to improve accuracy. Specifically, we devise a depth-aware ground region refinement technique that selectively focuses on ground regions near pedestrians during classification, effectively filtering out distant areas that could mislead the classification. Experimental results on a public dataset recorded in school zones demonstrate improvements in classification performance, particularly in precision. This depth-aware approach could improve pedestrian safety monitoring in smart cities by enabling more accurate situation assessment in complex visual environments.