Location and Semantic Separation Attention Based Lightweight Visual Object Tracking Algorithm

With the continuous development of the large deep model, the backbone of Siamese-based visual object tracking is strengthening and the number of parameters is increasing. This has led to doubling of the model training time and cost, making deployment of the model on edge devices challenging. This paper focuses on improving the ability of lightweight models to extract target location and semantic information and proposes a lightweight visual object tracking algorithm based on the location and semantic separation attention mechanism. First, the normalized attention mechanism is improved by combining horizontal and vertical convolutions to construct the position attention and embedding it into the shallow features of the backbone network to extract the target position in the formation. Subsequently, the squeeze-and-excitation network and channel direction normalized attention are fused with the deep features of the backbone network to extract semantic information. In contrast to the previous studies on attention mechanism, this study uses the properties of shallow features that are conducive to spatial information extraction and deep features that are conducive to semantic feature extraction in the network to separate location attention and semantic attention and improves the algorithm's ability to extract the target location and semantic information without significantly increasing the number of parameters. Experimental results on a general tracking dataset demonstrate that the proposed algorithm can improve the precision and success rate of a tracking algorithm based on a lightweight Siamese network.