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Title Pedrisknet: Classifying Pedestrian Situations In Surveillance Videos For Pedestrian Safety Monitoring In Smart Cities
ID_Doc 41546
Authors Kim D.H.; Moon J.
Year 2024
Published Proceedings - IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS, 2024
DOI http://dx.doi.org/10.1109/AVSS61716.2024.10672588
Abstract In the context of smart cities, pedestrian safety enhancement through visual AI-based technology is crucial. While pedestrian detection and tracking have been extensively studied, classifying pedestrian situations for immediate risk assessment remains challenging. Existing methods often fail to consider the broader context of unsafe situations of pedestrians or rely solely on pedestrian detection. To address this gap, we propose a novel pedestrian situation classification method incorporating ground region estimation and multi-modal fusion. By utilizing semantic segmentation and temporal consistency, we estimate ground region maps to mitigate occlusion effects. The proposed method fuses multi-modal features from the local surroundings and ground regions, allowing for accurate classification of pedestrian situations. Experimental results on a public dataset recorded in school zones demonstrate superior performance compared to baseline models. This approach holds significant potential for improving pedestrian safety in smart cities, enabling proactive measures and interventions to mitigate risks and enhance overall road safety. © 2024 IEEE.
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