To overcome the problems of small target occupation and vulnerability to interference of complex background information in the UAV video tracking process, an adaptive fusion network-based UAV target tracking algorithm is proposed. First, a deep network model is constructed based on the receptive field block and the residual network, which can effectively extract target features and enhance the effective receptive field of the features. Second, a multi-scale adaptive fusion network is proposed, which can adaptively fuse the semantic features of the deep network and detailed features of the shallow network to enhance the expression capability of the features. Finally, the fused target features are input into the correlation filtering model, and the maximum confidence score of the response map is calculated to determine the tracking target location. The simulation experimental results show that the algorithm achieves a high rate of tracking success and accuracy, and can effectively improve the performance of UAV target tracking algorithm.
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