[论文解读] Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications

Ruimin Ke, Zhiyong Cui+4 more|arXiv (Cornell University)|Aug 2, 2020

Video Surveillance and Tracking Methods参考文献 29被引用 5

一句话总结

本文提出了一种基于边缘计算的系统，利用车载行车记录仪视频流实现实时近碰撞检测，通过多线程处理和对象检测与跟踪输出的线性复杂度建模，实现高效处理。在十万小时真实世界数据上评估，该系统在减少带宽、存储和人工成本的同时，实现了高效率、高精度和强可迁移性，能够实时过滤无关视频。

ABSTRACT

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.

研究动机与目标

• 解决在智能交通系统中从大规模视频数据中提取近碰撞事件所面临的高计算、通信和存储需求。
• 克服现有近碰撞检测方法在大规模部署中效率低下且缺乏可迁移性的缺陷。
• 实现在车辆边缘设备上直接对行车记录仪视频流进行实时处理，最大限度减少对云基础设施的依赖。
• 开发一种不仅能捕捉碰撞事件，还能同时捕获车辆速度、轨迹和碰撞时间等丰富上下文数据的系统。
• 通过与相机参数无关的设计，确保在多种车辆和摄像头配置下的兼容性。

提出的方法

• 在边缘设备上实现多线程系统架构，以支持视频流的并发处理和实时分析。
• 采用线性复杂度算法对目标检测与跟踪输出进行建模，确保可扩展性和低延迟处理。
• 处理目标检测生成的边界框，计算关键指标如碰撞时间、车辆速度和轨迹。
• 提取并存储结构化数据，包括道路使用者类型、事件位置、刹车开关状态和油门输入，与事件视频一同保存。
• 设计具备向后兼容性，适用于多种车型和摄像头配置，确保广泛部署潜力。
• 在设备端实时过滤非事件视频，显著降低数据传输和存储需求。

实验结果

研究问题

• RQ1 边缘计算能否在不依赖云基础设施的前提下，实现实时、设备端的行车记录仪视频流近碰撞检测？
• RQ2 该系统在保留关键安全相关数据的同时，能否高效过滤无关视频内容？
• RQ3 该系统在不同车辆和摄像头配置下的鲁棒性和可迁移性如何？
• RQ4 在真实驾驶条件下，该系统检测近碰撞事件的准确性和可靠性如何？
• RQ5 该系统能否实时同步提取包括速度、轨迹、刹车状态等丰富上下文数据以及事件视频？

主要发现

• 该系统成功处理了来自两辆汽车和四辆公交车的超过十万小时真实世界视频数据，表现出高可靠性和低延迟。
• 通过实时过滤无关视频，显著降低了网络带宽、数据存储和人工劳动成本。
• 通过真实世界测试，该方法在检测近碰撞事件方面表现出高精度，事件检测过程中未报告任何失败。
• 该系统对相机参数不敏感，在不同车型和配置下均保持一致的性能表现。
• 系统以高保真度捕获并存储了包括碰撞时间、车辆速度、刹车开关状态和油门输入在内的宝贵补充数据。
• 该系统是少数最早实现边缘计算用于实时交通视频分析的实例之一，具备强大的可迁移性和高效性。