This paper aims to introduce an approach to the organization of road traffic monitoring by the means of unmanned aerial vehicles (UAVs), which is based on the automatic situation control of UAVs. The research includes analysis of existing methods of on-board automatic detection of emergency and abnormal traffic situations with UAV artificial vision systems (AVS), preliminary classification of these situations including the allocation of emergencies and disastrous situations. The paper presents the choice of UAV controls in compliance with the recognized situation. The traffic situation identification method introduced in the paper is based on Bayes and Neyman-Pearson criterion. Furthermore, the research involves the analysis of the existing approaches to the detection of moving and stationary vehicles by the means of UAV AVS. The paper proposes vehicles detection method based on the image segmentation, along with the use of machine learning methods, particularly the artificial neural network method known as Deep Learning. The research provides solutions for vehicle tracking and velocity detection problems in order to describe traffic situations. The proposed approach contributes to the efficiency of UAV in road traffic monitoring by means of the management and detection processes automation.
Kim, N. V., & Chervonenkis, M. A. (2015). Situation control of unmanned aerial vehicles for road traffic monitoring. Modern Applied Science, 9(5), 1–13. https://doi.org/10.5539/mas.v9n5p1