A novel Siamese network object tracking algorithm based on tensor space mapping and memory-learning mechanism.

• Build a correlation model of adjacent features to guide the feature update of subsequent states. • The updated features are fused with the template features to filter the discriminative attribute features. • Simulate the saccade mechanism to solve the problem of missing fine-grained features in batch information processing. • Filter fine-grained features by using hierarchical guidance and multiple traversals. The tracker is a core component of the tracking algorithm, but it is difficult to identify the object, which is a challenge to improve the tracking accuracy. This paper proposes a Siamese network-based tracking algorithm based on tensor space mapping and memory-learning mechanisms. Firstly, the source image is mapped to the tensor space to serialize the feature distributions. Then the gating mechanism is used to extract the association information about the adjacent state, which guides the update of the subsequent state, and the interactive information on the objects is used to locate the object. On this basis, a memory-learning module is built to traverse and extract the fine-grained features, which can filter the semantic information of the object learned by the tracker. As a result, the tracking accuracy is enhanced. The experiments show that the proposed algorithm has better performance than that of the comparison methods in the OTB100 data set and the VOT data set. [ABSTRACT FROM AUTHOR]