This paper presents an efficient visual tracking algorithm based on a structural local sparse appearance model and an adaptive template update strategy. The method leverages both spatial and partial information of the target by employing an alignment-pooling method across overlapping local image patches within the target region. This approach enhances the accuracy of target localization and improves robustness against partial occlusion. Additionally, the template update strategy combines incremental subspace learning and sparse representation to adapt the template to the target's appearance changes, reducing drift and the influence of occluded templates. The proposed algorithm is evaluated on challenging benchmark image sequences, demonstrating superior performance compared to several state-of-the-art methods in terms of position errors and success rates. The effectiveness and robustness of the proposed method are validated through quantitative and qualitative evaluations, showing its ability to handle various challenges such as occlusion, illumination changes, and background clutter.This paper presents an efficient visual tracking algorithm based on a structural local sparse appearance model and an adaptive template update strategy. The method leverages both spatial and partial information of the target by employing an alignment-pooling method across overlapping local image patches within the target region. This approach enhances the accuracy of target localization and improves robustness against partial occlusion. Additionally, the template update strategy combines incremental subspace learning and sparse representation to adapt the template to the target's appearance changes, reducing drift and the influence of occluded templates. The proposed algorithm is evaluated on challenging benchmark image sequences, demonstrating superior performance compared to several state-of-the-art methods in terms of position errors and success rates. The effectiveness and robustness of the proposed method are validated through quantitative and qualitative evaluations, showing its ability to handle various challenges such as occlusion, illumination changes, and background clutter.