CotNet target tracking algorithm based on attention mechanism and context-awareness

In recent years, Siamese network algorithms based on deep learning classes have achieved better tracking accuracy and speed and become one of the research hotspots in the field of target tracking. However, the traditional Siamese network algorithm lacks a holistic view of the target and extracts shallow features, making it easy to lose track of the target in complex environments. The paper proposes a Contextual transformer network for visual recognition (CotNet) target tracking algorithm based on attentional mechanisms and contextual awareness to address this. The paper innovatively uses the CotNet50 network as the backbone network and adopts a residual network variant design scheme with a self-attention mechanism, which can enhance the feature representation capability of the network model and improve the performance of the algorithm. In addition, to handle changes in appearance during target tracking, an efficient channel attention module, and a global contextual feature module are embedded in the backbone network branch to enhance the network's overall perception of the target and improve the algorithm's tracking accuracy. The experimental results of this paper's algorithm on the VOT2018 data show that the accuracy, robustness, and EAO (Expected Average Overlap) are improved by 7.3%, 13.95%, and 11.9% respectively compared to SiamFC. It has good tracking results when dealing with complex scenes on the OTB100 dataset.