A Target Tracking Algorithm Based on Hybrid Pooling and Transformer

To enhance the robustness and accuracy of target tracking in complex backgrounds and dynamic scenes, this paper presents a target tracking algorithm that integrates hybrid pooling with a Transformer module. The Transformer module captures global context information, effectively mitigating background interference while strengthening the discriminative power of target features. The hybrid pooling technique improves the model's spatial context modeling capability by fusing multi-scale features. Experimental results demonstrate that the proposed method performs exceptionally well on the OTB100 and GOT-10k datasets.