Real-Time Tracking Algorithm for Aerial Vehicles Using Improved Convolutional Neural Network and Transfer Learning

To address the poor image feature extraction ability, excessive tracking time, and low accuracy of traditional real-time algorithms for aerial tracking of vehicle traffic, a real-time tracking algorithm using improved convolutional neural network (CNN) and transfer learning was proposed herein. In this algorithm, first, the aerial vehicle images are matched with sample images for the image offset calibration. Second, the CNN parameters are initialized by constructing a filter set, and transfer learning is employed to construct a CNN pre-training model. Third, a deep convolution feature extraction structure map is designed to extract the depth features of images. Finally, based on the depth features, the target vehicle motion model is established, the similarity between the target and candidate models is calculated, and the real-time tracking of aerial vehicles is completed. The results show that the image correction accuracy of the proposed algorithm is as high as 92%. The algorithm yields satisfactory results in terms of feature extraction and calculation accuracy. Moreover, it has a small overall error, the average tracking time required by it is only 22.8 s, and its false negative rate is as low as 0.4%. Therefore, the proposed algorithm has considerable potential practical application.