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:
this https URL. Video demonstrations are provided here:
this https URL and here: this https URL.

本文介绍了一种名为 TAU 的智能交通系统，使用人工智能和无人机技术来实现自动化的交通分析和理解，可以高效准确地进行车辆检测、追踪、速度计算以及交通区域内车辆数量的计算，并且能从原始数据中提取 24 种类型的信息。