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.

翻译：Pedestrians人由于各种原因在公路上,特别是没有标志的十字路口面临死亡或严重伤害的风险。迄今为止,大量各种研究都报告了基于视觉的交通安全系统。然而,许多研究要求对交通视频的数量进行人工检查,以可靠地获得交通相关物体行为因素。在本文中,我们提议建立一个自动和简单化的系统,以便有效地从在公路上部署的视频传感器中提取物体行为特征。我们对这些特征进行了基本统计分析,并表明这些特征如何有助于监测交通行为。我们确认拟议系统的可行性,将我们的原型应用到南韩奥山市的两个未标志的十字路口。我们的结论是,通过简单的统计分析来比较这两个地区的车辆和行人的行为。这一研究表明,连接的视频传感器网络有可能为智能城市提供可操作的数据,以改善危险道路环境中的行人安全。