Visual tracking algorithm based on template updating and dual feature enhancement

Aiming at the problem of tracking failure due to target deformation, flipping and occlusion in visual tracking, a template updating algorithm based on image structural similarity is proposed by dynamically updating the template to adapt to the changes of the target during tracking. The tracking feature enhancement module and segmentation feature enhancement module are also designed based on the SiamMask network. The tracking feature enhancement module consists of non-local operations and convolutional downsampling, which is used to establish contextual correlation, enhance the target features, suppress the background interference, improve the tracking robustness, and solve the feature attenuation problem due to the occlusion of the target. The segmentation feature enhancement module introduces the convolutional block attention module and deformable convolution to improve the network’s ability to capture channel and spatial features, adaptively learn the shape and contour information of the target, and enhance the network’s segmentation accuracy of the tracked target, which in turn improves the tracking accuracy. In comparison to the baseline SiamMask, experiments demonstrate that the proposed algorithm performs well and steadily in solving the aforementioned problems, improving the expected average overlap rate by 0.052, 0.053, and 0.025 and the robustness by 0.06, 0.079, and 0.156 on the VOT2016, VOT2018, and VOT2019 datasets, respectively. It also achieves a real-time speed of 91 frames per second on average.