To address challenges in single-target tracking under complex scenarios such as target deformation， occlusion， similar interference， and out-of-view situations， a novel tracking algorithm is proposed. Building on the Staple algorithm， the method optimizes pixel weight assignment using a two-dimensional Gaussian function and enhances the color histogram to improve target-background distinguishability. An adaptive fusion mechanism based on the Peak Side Lobe Ratio （PSR） is introduced to combine HOG and color features， with carefully selected fusion coefficients ensuring feature reliability. The target's optimal center position is determined by analyzing the distance between the current and previous frame centers， alongside the maximum composite response， effectively mitigating interference from similar targets. Target loss or occlusion is identified using composite response， HOG features， and Average Peak-to-Correlation Energy （APCE）， maintaining the target frame's position and enabling timely re-tracking upon reappearance. A template update strategy combining past and current frame information further enhances tracking accuracy. Tests on the OTB100 dataset with deformation， occlusion， and out-of-view scenarios show that the improved algorithm increases overall and specific attribute success rates （deformation， occlusion， out-of-view） by 1.8%， 3.3%， 2%， and deformation precision by 9% compared to the Staple algorithm. On the VOT16 dataset， the overlap rate for overall and occlusion attributes improves by 0.022 2 and 0.019 6 respectively， meeting the demands of target tracking in complex scenarios.