Visual target tracking based on fractional – order bidirectional hybrid attentional feature fusion

This paper mainly designs and implements target tracking based on fractional – order feature fusion to solve tracking drift and tracking box jumping in complex scenes. Firstly, the starting point and overall structure of the model design were introduced. Secondly, in response to the low information utilization of the feature extraction network in the target tracking framework at the local scale of the target, a fractional – order node function based attention CNN hybrid attention extraction module was proposed to improve the robustness of target tracking. Finally, overall performance was quantitatively and qualitatively evaluated on multiple evaluation datasets with various advanced trackers. The results showed that the algorithm proposed in this paper had a high tracking advantage under severe changes in scale morphology, dynamic blurring, and similar interference attributes.