Abstract
The advantages of UAV video in flexibility, traceability, easy-operation, and abundant information make it a popular and powerful aerial tool applied in traffic monitoring in recent years. This paper proposed a systematic approach to detect and track vehicles based on the YOLO v3 model and the deep SORT algorithm for further extracting key traffic parameters. A field experiment was implemented to provide data for model training and validation to ensure the accuracy of the proposed approach. In the experiment, 5400 frame images and 1192 speed points were collected from two test vehicles equipped with high-precision GNSS-RTK and onboard OBD after completion of seven experimental groups with a different height (150 m to 500 m) and operating speed (40 km/h to 90 km/h). The results indicate that the proposed approach exhibits strong robustness and reliability, due to the 90.88% accuracy of object detection and 98.9% precision of tracking vehicle. Moreover, the absolute and relative error of extracted speed falls within ±3 km/h and 2%, respectively. The overall accuracy of the extracted parameters reaches up to 98%.
Highlights
Traffic monitoring is a fundamental step for intelligent transportation systems (ITS)applications
The methodology is mainly focused on vehicle detection and tracking
While unmanned aerial vehicle (UAV)-based monitoring exhibits advantages including flexibility, efficiency, large view range, and traceability, this topic has attracted broad attention in the field of traffic monitoring considering the requirements for real-time traffic management and control
Summary
Traffic monitoring is a fundamental step for intelligent transportation systems (ITS)applications. Vehicle level traffic data usually comes from two types of sensors: Eulerian (fixed) sensors and Lagrangian (mobile) sensors. The former includes radars [1], inductive loop detectors [2] or magnetometers [3], visible or infrared traffic cameras [4], while the later usually means GPS-based or other techniques supported by probe vehicles [5,6,7]. There are some limitations for both fixed and mobile sensors while monitoring traffic state information. It is sometimes difficult to obtain traffic data at vehicle’s level considering the situation with sparse layout of fixed detectors or limited sampling rate of mobile detectors (such as vehicle trajectory data)
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