基于形态自适应网络的无人机目标跟踪方法

To solve the problems such as the change of target direction, change of target occlusion and lack of sample diversity in the process of target tracking based on UAV images, this paper proposes a UAV aerial image target tracking algorithm based on the adaptive morphological network. First, the data-driven method is used to expand datasets, and multi rotation angle samples and occlusion samples are added to improve the diversity of samples. The proposed adaptive morphological network improves the deep belief network by rotating invariant constraints to extract deep features with strong representativeness, which enables the model to automatically adapt to the changes of target morphology. The deep feature transformation algorithm is used to obtain the pre-location area of the target to be detected. The target is located adaptively and accurately by the search agent based on the Q-learning algorithm. The category information of the tracking target is extracted by using the deep forest classifier, and the target tracking results with high precision are obtained. Comparative experiments are then carried out on several datasets. The experimental results show that the algorithm can achieve high tracking accuracy, adapt to the change of target angle and occlusion, and has good accuracy and robustness.