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

TL;DR

This paper presents an automated system for extracting behavioral features from video sensors to analyze traffic safety at crosswalks, demonstrating its feasibility through real-world deployment and statistical analysis.

Contribution

The study introduces a novel automated method for extracting traffic object behaviors from video data, reducing manual inspection and enhancing safety monitoring capabilities.

Findings

Feasibility confirmed through deployment at two crosswalks

Statistical analysis reveals behavioral differences between locations

Potential for smart city safety improvements

Abstract

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.