Traffic near-crash events serve as critical data sources for various smart transportation applications, such as being surrogate safety measures for traffic safety research and corner case data for automated vehicle testing. However, there are several key challenges for near-crash detection. First, extracting near-crashes from original data sources requires significant computing, communication, and storage resources. Also, existing methods lack efficiency and transferability, which bottlenecks prospective large-scale applications. To this end, this paper leverages the power of edge computing to address these challenges by processing the video streams from existing dashcams onboard in a real-time manner. We design a multi-thread system architecture that operates on edge devices and model the bounding boxes generated by object detection and tracking in linear complexity. The method is insensitive to camera parameters and backward compatible with different vehicles. The edge computing system has been evaluated with recorded videos and real-world tests on two cars and four buses for over ten thousand hours. It filters out irrelevant videos in real-time thereby saving labor cost, processing time, network bandwidth, and data storage. It collects not only event videos but also other valuable data such as road user type, event location, time to collision, vehicle trajectory, vehicle speed, brake switch, and throttle. The experiments demonstrate the promising performance of the system regarding efficiency, accuracy, reliability, and transferability. It is among the first efforts in applying edge computing for real-time traffic video analytics and is expected to benefit multiple sub-fields in smart transportation research and applications.
翻译:首先,从原始数据源中提取近悬崖需要大量的计算、通信和储存资源;此外,现有方法缺乏效率和可转移性,从而阻碍可能的大规模应用;为此,本文件利用边缘计算的力量,通过实时处理现有破碎摄像头视频流,来应对这些挑战;我们设计了一个多轨系统结构,在边缘装置上操作,并模拟线性复杂天体探测和跟踪生成的捆绑盒;这种方法对摄像参数不敏感,落后于不同的车辆;现有方法缺乏效率和可转移性,从而阻碍可能的大规模应用;为此,本文件利用边缘计算的力量,通过实时处理现有破碎摄像头的视频流,解决这些挑战;我们设计了一个多轨式系统结构,不仅在边缘装置上运行,而且模拟线性物体探测和跟踪生成的捆绑盒;这种方法对摄像参数不敏感,而且与不同的车辆的落后;对两部汽车和四部大客车进行有记录的视频和实时测试,从而节省劳动力成本、处理时间、网络带宽度和数据存储;我们不仅收集了事件视频,而且还收集了其他宝贵的数据,例如:在车辆轨道运行轨道运行的预期速度、车辆运行速度、预变速性试验中,对车辆的预期速度和速度转换效率,对车辆的预期速度和速度、记录、记录、记录速度分析,对车辆的精确性试验,对车辆的精确性试验,对车辆的精确性试验,对车辆运行效率进行实时转换速度和速度和速度的精确性试验,对车辆的精确性试验,对车辆的精确性研究,对车辆的精确性试验,对车辆的精确性研究,对速度和速度进行。