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.