Action detection and public traffic safety are crucial aspects of a safe community and a better society. Monitoring traffic flows in a smart city using different surveillance cameras can play a significant role in recognizing accidents and alerting first responders. The utilization of action recognition (AR) in computer vision tasks has contributed towards high-precision applications in video surveillance, medical imaging, and digital signal processing. This paper presents an intensive review focusing on action recognition in accident detection and autonomous transportation systems for a smart city. In this paper, we focused on AR systems that used diverse sources of traffic video capturing, such as static surveillance cameras on traffic intersections, highway monitoring cameras, drone cameras, and dash-cams. Through this review, we identified the primary techniques, taxonomies, and algorithms used in AR for autonomous transportation and accident detection. We also examined data sets utilized in the AR tasks, identifying the main sources of datasets and features of the datasets. This paper provides potential research direction to develop and integrate accident detection systems for autonomous cars and public traffic safety systems by alerting emergency personnel and law enforcement in the event of road accidents to minimize human error in accident reporting and provide a spontaneous response to victims
翻译:使用不同的监视摄像机监测智能城市交通流量,在识别事故和提醒第一反应者方面可以发挥重要作用。在计算机愿景任务中使用行动识别(AR),有助于在视频监视、医疗成像和数字信号处理方面实现高精度应用;本文件集中审查了智能城市在事故检测和自主运输系统中的行动识别和自主运输系统;在本文件中,我们侧重于使用各种交通视频捕捉来源的AR系统,例如交通交叉点、高速公路监测摄像机、无人机摄像机和破碎摄像头的静态监视摄像机的AR系统。通过这次审查,我们查明了AR用于自动运输和事故探测的主要技术、分类和算法。我们还审查了AR任务中使用的数据集,查明了数据集的主要来源和数据集特征。本文件提供了潜在的研究方向,以开发和整合自主汽车和公共交通安全系统的事故检测系统,提醒应急人员和执法人员在发生道路交通事故时尽量减少事故报告中的人为错误,并对受害者作出自发反应。