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Conference Paper
Depth-aware Pedestrian Situation Classification for Enhanced Safety Monitoring in Smart Cities
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- Authors
- Dae Hoe Kim, Jinyoung Moon
- Issue Date
- 2025-08
- Citation
- International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2025, pp.1-5
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Classification Performance, Classification method, Depth information, Detection and tracking, Pedestrian safety, Public Datasets, Public safety, Safety Monitoring, Situation Assessment, Smart city, urban environments