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In this paper, a novel vehicle tracking and speed estimation method based on aerial videos is developed. The main objective of this research is to investigate how depth features of a vehicle extracted from aerial videos by a wide residual network could be used to realize accurate tracking for estimating the speed of the vehicle. The salient characteristics of the proposed method are: (1) Traffic analysis and robust vehicle detection are possible by virtue of the use of deep neural networks. (2) Employing the state-of-the-art object detector, namely the You Only Look Once (YOLO) network, a tracking-by-detection method is then designed to detect and track vehicles. (3) The error rate of data association is reduced by associating the tracking process with a vehicle re-identification algorithm whereby the motion features are combined with depth features of the vehicle. (4) Considering the varying Unmanned Aerial Vehicle (UAV) altitude and video resolution, a novel exponential mapping model of the vehicle speed from the pixel space to the real world is presented. The accuracy of speed estimation is effectively improved by employing the least squares algorithm to fit the measurement data. Simulation results and comparative studies with the state-of-the-art methods demonstrate superior performance of the proposed approach for vehicle tracking and speed estimation.
Chen et al. (Tue,) studied this question.