Automated Object Behavioral Feature Extraction for Potential Risk Analysis based on Video Sensor

Pedestrians are exposed to risk of death or serious injuries on roads, especially unsignalized crosswalks, for a variety of reasons. To date, an extensive variety of studies have reported on vision based traffic safety system. However, many studies required manual inspection of the volumes of traffi...

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Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Noh, Byeongjoon, Dongho Ka, Noh, Wonjun, Yeo, Hwasoo
Format: Article
Language:English
Subjects:
Automation
Feature extraction
Inspection
Pedestrian safety
Pedestrians
Risk analysis
Roads & highways
Sensors
Statistical analysis
Traffic accidents & safety
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Summary:Pedestrians are exposed to risk of death or serious injuries on roads, especially unsignalized crosswalks, for a variety of reasons. To date, an extensive variety of studies have reported on vision based traffic safety system. However, many studies required manual inspection of the volumes of traffic video to reliably obtain traffic related objects behavioral factors. In this paper, we propose an automated and simpler system for effectively extracting object behavioral features from video sensors deployed on the road. We conduct basic statistical analysis on these features, and show how they can be useful for monitoring the traffic behavior on the road. We confirm the feasibility of the proposed system by applying our prototype to two unsignalized crosswalks in Osan city, South Korea. To conclude, we compare behaviors of vehicles and pedestrians in those two areas by simple statistical analysis. This study demonstrates the potential for a network of connected video sensors to provide actionable data for smart cities to improve pedestrian safety in dangerous road environments.
ISSN:2331-8422