无人机视频中的实时交通队尾检测与跟踪
Real-Time Traffic End-of-Queue Detection and Tracking in UAV Video
AI总结：
本文提出一种基于无人机视频和图像处理的实时交通队尾检测方法，利用背景减除与斑点检测动态定位施工区车辆队尾，以辅助警示标志快速调整并提升交通安全。
摘要：
高速公路施工区容易出现过度的机动车辆积聚，这需要动态的施工区警示标志来预防事故。施工区标志根据车辆队尾的位置进行设置，而该位置通常变化迅速。迄今为止，对无人机（UAV）拍摄视频中运动目标的检测已得到广泛研究，并被用于包括交通监控在内的众多应用。与固定交通摄像头不同，无人机能够以实时方式监控施工区的交通，并且成本效益更高。本研究提出一种方法，作为概念验证，通过处理无人机拍摄的高速公路施工区实时视频画面来检测交通队尾（EOQ）。在视频中通过图像处理检测EOQ，其中包括背景减除和斑点检测方法。这种对车辆EOQ的动态定位将能够更快、更准确地为驾驶员重新安置施工区警示标志，从而减少施工区死亡事故。该方法还可用于在其他道路或交叉口检测车辆EOQ并通知驾驶员，在这些地方车辆会因特殊事件、交通拥堵、施工或事故而迅速积聚。
英文摘要：
Highway work zones are susceptible to undue accumulation of motorized vehicles which calls for dynamic work zone warning signs to prevent accidents. The work zone signs are placed according to the location of the end-of-queue of vehicles which usually changes rapidly. The detection of moving objects in video captured by Unmanned Aerial Vehicles (UAV) has been extensively researched so far, and is used in a wide array of applications including traffic monitoring. Unlike the fixed traffic cameras, UAVs can be used to monitor the traffic at work zones in real-time and also in a more cost-effective way. This study presents a method as a proof of concept for detecting End-of-Queue (EOQ) of traffic by processing the real-time video footage of a highway work zone captured by UAV. EOQ is detected in the video by image processing which includes background subtraction and blob detection methods. This dynamic localization of EOQ of vehicles will enable faster and more accurate relocation of work zone warning signs for drivers and thus will reduce work zone fatalities. The method can be applied to detect EOQ of vehicles and notify drivers in any other roads or intersections too where vehicles are rapidly accumulating due to special events, traffic jams, construction, or accidents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
