Various mainstream target tracking algorithms based on siamese networks are gradually becoming a trend in the field of deep learning tracking due to their concurrent advantages of accuracy and speed. Most siamese network-based trackers describe the tracking of a target object as a similar matching problem, and these trackers have achieved more advanced performance in several public tests. Most trackers often suffer from tracking drift or performance degradation owing to the non-updating of the template in the first frame and the target appearance encounters disturbing environments such as occlusion and drastic deformation. To address the current problem, this paper proposes an algorithm for fusion of multi-branch modules based on siamese network to achieve the fusion of target templates for updating, which improves the tracker's anti-jamming ability and the network structure is anchor-free. Meanwhile, this paper designs a new fusion module that fuses templates by inserting weight tensor method for multi-template fusion and optimises the results by complementary weight tensors. This method is practiced in SiamFC++ algorithm, where the target dataset is input and features are extracted, and then classification and regression operations are performed by fusion of multi-branch modules to get the predicted position of the target.
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