Modern vehicles equip dashcams that primarily collect visual evidence for traffic accidents. However, most of the video data collected by dashcams that is not related to traffic accidents is discarded without any use. In this paper, we present a use case for dashcam videos that aims to improve driving safety. By analyzing the real-time videos captured by dashcams, we can detect driving hazards and driver distractedness to alert the driver immediately. To that end, we design and implement a Distributed Edge-based dashcam Video Analytics system (DEVA), that analyzes dashcam videos using personal edge (mobile) devices in a vehicle. DEVA consolidates available in-vehicle edge devices to maintain the resource pool, distributes video frames for analysis to devices considering resource availability in each device, and dynamically adjusts frame rates of dashcams to control the overall workloads. The entire video analytics task is divided into multiple independent phases and executed in a pipelined manner to improve the overall frame processing throughput. We implement DEVA in an Android app and also develop a dashcam emulation app to be used in vehicles that are not equipped with dashcams. Experimental results using the apps and commercial smartphones show that DEVA can process real-time videos from two dashcams with frame rates of around 22~30 FPS per camera within 200 ms of latency, using three high-end devices.

翻译：现代车辆配备的行车记录仪主要收集交通事故的视觉证据。然而，大部分与交通事故无关的行车记录仪视频数据未经任何使用即被丢弃。本文提出一种旨在提升驾驶安全性的行车记录仪视频应用案例。通过分析行车记录仪捕获的实时视频，系统可检测驾驶危险与驾驶员分心行为，并及时向驾驶员发出警报。为此，我们设计并实现了分布式边缘行车记录仪视频分析系统（DEVA），该系统利用车辆内的个人边缘（移动）设备分析行车记录仪视频。DEVA整合可用的车载边缘设备以维护资源池，根据各设备资源可用性分配待分析视频帧，并动态调节行车记录仪帧率以控制系统整体负载。整个视频分析任务被划分为多个独立阶段，采用流水线方式执行以提升整体帧处理吞吐量。我们将DEVA实现为Android应用，并开发了行车记录仪仿真应用供未配备行车记录仪的车辆使用。通过应用测试与商用智能手机的实验结果表明：使用三台高端设备时，DEVA可在200毫秒延迟内处理来自两个行车记录仪的实时视频，每台相机帧率约为22~30 FPS。