Automated Object Behavioral Feature Extraction for Potential Risk Analysis based on Video Sensor
This work addresses pedestrian safety at unsignalized crosswalks for smart cities, but it is incremental as it builds on existing vision-based traffic safety systems by automating feature extraction.
The authors tackled the problem of manually inspecting traffic video for safety analysis by proposing an automated system to extract object behavioral features from video sensors at unsignalized crosswalks, demonstrating feasibility through a prototype applied in Osan city, South Korea, with comparisons of vehicle and pedestrian behaviors via statistical analysis.
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.