Combining Deep and Handcrafted Image Features for Vehicle Classification in Drone Imagery

Using unmanned aerial vehicles (UAVs) as devices for traffic data collection exhibits many advantages in collecting traffic information. This paper presents an efficient method based on the deep learning and handcrafted features to classify vehicles taken from drone imagery. Experimental results show that compared to classification algorithms based on pre-trained CNN or hand-crafted features, the proposed algorithm exhibits higher accuracy in vehicle recognition at different UAV altitudes with different view scopes, which can be used in future traffic monitoring and control in metropolitan areas.