Despite recent advances in vehicle safety technologies, road traffic accidents still pose a severe threat to human lives and have become a leading cause of premature deaths. In particular, crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. Therefore, we propose a comprehensive analytical model for pedestrian potential risk using video footage gathered by road security cameras deployed at such crossings. The proposed system automatically detects vehicles and pedestrians, calculates trajectories by frames, and extracts behavioral features affecting the likelihood of potentially dangerous scenes between these objects. Finally, we design a data cube model by using the large amount of the extracted features accumulated in a data warehouse to perform multidimensional analysis for potential risk scenes with levels of abstraction, but this is beyond the scope of this paper, and will be detailed in a future study. In our experiment, we focused on extracting the various behavioral features from multiple crosswalks, and visualizing and interpreting their behaviors and relationships among them by camera location to show how they may or may not contribute to potential risk. We validated feasibility and applicability by applying it in multiple crosswalks in Osan city, Korea.

中文翻译：

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基于行为特征自动提取的基于视觉的智能安全城市行人潜在风险分析

尽管车辆安全技术最近取得了进步，但道路交通事故仍然对人类生命构成严重威胁，并已成为导致过早死亡的主要原因。特别是，人行横道对行人构成了重大威胁，但是我们缺乏密集的行为数据来调查行人面临的风险。因此，我们使用部署在此类十字路口的道路安全摄像机收集的录像，提出了行人潜在风险的综合分析模型。拟议的系统自动检测车辆和行人，按帧计算轨迹，并提取影响这些物体之间潜在危险场景可能性的行为特征。最后，我们通过使用数据仓库中积累的大量提取特征来设计数据多维数据集模型，以对具有抽象级别的潜在风险场景进行多维分析，但这超出了本文的范围，将在以后进行详细介绍学习。在我们的实验中，我们专注于从多个人行横道中提取各种行为特征，并通过摄像头位置可视化并解释它们的行为和它们之间的关系，以显示它们可能会或可能不会助长潜在风险。我们通过将其应用于韩国乌山市​​的多个人行横道中，验证了其可行性和适用性。我们专注于从多个人行横道中提取各种行为特征，并通过摄像头位置可视化并解释它们的行为和它们之间的关系，以显示它们可能会或可能不会助长潜在风险。我们通过将其应用于韩国乌山市​​的多个人行横道中，验证了其可行性和适用性。我们专注于从多个人行横道中提取各种行为特征，并通过摄像头位置可视化并解释它们的行为和它们之间的关系，以显示它们可能会或可能不会助长潜在风险。我们通过将其应用于韩国乌山市​​的多个人行横道中，验证了其可行性和适用性。