Depth-aware Pedestrian Situation Classification for Enhanced Safety Monitoring in Smart Cities

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.