Above the roads: Unleashing unmanned aerial vehicles and image processing for traffic analysis

Abstract

Road traffic surveillance using unmanned aerial vehicles is a practice that can be found especially in the field of intelligent vehicle management, which is still in the early stages of research and application. This paper presents three methods of analyzing traffic data. One method is a conventional one, based on Doppler radar detection and the other two methods analyze images captured by unmanned aerial vehicles, being based on deep learning techniques. After acquiring the images, they went through a complex processing process to eliminate noise and improve the clarity of the image, then the identification of the vehicles was done by recognizing moving objects and highlighting them either through a bounding box or through labelling. The quality of images obtained from unmanned aerial vehicles is similar to the quality of images obtained from fixed surveillance cameras. The comparative analysis of the results obtained through image processing, together with those obtained through a conventional method of traffic analysis, the Doppler radar, highlighted the fact that video detection used in intelligent vehicle management is a method that both researchers and local authorities can rely on the performance of traffic studies or the analysis of traffic incidents and accidents.