Smart traffic engineering and intelligent transportation services are in increasing demand from governmental authorities to optimize traffic performance and thus reduce energy costs, increase the drivers' safety and comfort, ensure traffic laws enforcement, and detect traffic violations. In this paper, we address this challenge, and we leverage the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traffic video analytics, we propose an automated object detection and tracking pipeline from video processing to advanced traffic understanding using high-resolution UAV images. TAU combines six main contributions. First, it proposes a pre-processing algorithm to adapt the high-resolution UAV image as input to the object detector without lowering the resolution. This ensures an excellent detection accuracy from high-quality features, particularly the small size of detected objects from UAV images. Second, it introduces an algorithm for recalibrating the vehicle coordinates to ensure that vehicles are uniquely identified and tracked across the multiple crops of the same frame. Third, it presents a speed calculation algorithm based on accumulating information from successive frames. Fourth, TAU counts the number of vehicles per traffic zone based on the Ray Tracing algorithm. Fifth, TAU has a fully independent algorithm for crossroad arbitration based on the data gathered from the different zones surrounding it. Sixth, TAU introduces a set of algorithms for extracting twenty-four types of insights from the raw data collected. The code is shared here: https://github.com/bilel-bj/TAU. Video demonstrations are provided here: https://youtu.be/wXJV0H7LviU and here: https://youtu.be/kGv0gmtVEbI.

翻译：智能交通工程与智能交通服务日益受到政府部门的重视，旨在优化交通性能以降低能源成本、提升驾驶员安全与舒适度、确保交通法规执行并检测交通违规行为。本文针对这一挑战，利用人工智能（AI）与无人机（UAV）技术，开发了一款名为TAU（Traffic Analysis from UAVs）的AI集成视频分析框架，用于自动化交通分析与理解。不同于以往的视频交通分析研究，我们提出了一套从视频处理到高级交通理解的自动化目标检测与跟踪流水线，采用高分辨率无人机图像。TAU包含六项核心贡献：第一，提出预处理算法，在不降低分辨率的前提下将高分辨率无人机图像适配为目标检测器的输入，从而确保从高质量特征中获取卓越检测精度（尤其针对无人机图像中小尺寸目标）。第二，提出车辆坐标校准算法，保障同一帧中不同图像裁剪块内的车辆唯一识别与跨块跟踪。第三，提出基于连续帧信息累积的速度计算算法。第四，采用射线追踪算法统计各交通区域的车辆数量。第五，基于各交叉口周边区域收集的数据，设计全自主的交叉口仲裁算法。第六，引入一组从原始数据中提取二十四类洞察的算法。代码共享于：https://github.com/bilel-bj/TAU。视频演示请见：https://youtu.be/wXJV0H7LviU 和 https://youtu.be/kGv0gmtVEbI。