This paper proposes a visual tracking method based on a structural local sparse appearance model and an adaptive template update strategy. The method uses a novel alignment-pooling approach to extract spatial and partial information from local image patches, which helps in accurately locating the target and handling occlusion. The sparse representation is combined with incremental subspace learning for template update, allowing the tracker to adapt to appearance changes and reduce the influence of occluded templates. The proposed method is evaluated on challenging benchmark sequences and shows favorable performance compared to several state-of-the-art methods. The method samples overlapped local image patches within the target region and uses a spatial layout to capture both spatial and partial information. The alignment-pooling method is used to compute similarity across local patches, which helps in more accurate target localization and occlusion handling. The dictionary for local sparse coding is generated from dynamic templates, which are updated online using both incremental subspace learning and sparse representation. This update strategy allows the tracker to account for appearance changes of the target and reduces the influence of occluded templates. The proposed method uses a structural local sparse appearance model that captures the structural information of the target object in terms of blocks. This model is able to handle partial occlusion by using sparse coding coefficients with the alignment-pooling method. The method also employs a template update strategy that combines incremental subspace learning and sparse representation to adapt the template to the appearance change of the target and reduce the influence of occluded templates. The method is evaluated on challenging benchmark sequences, including those with illumination variation, partial occlusion, pose variation, background clutter, and scale change. The results show that the proposed method performs favorably against several state-of-the-art tracking methods. The method is efficient and robust, making it suitable for real-world applications. The contributions of this work include the use of sparse codes of local image patches with spatial layout to model the appearance of the target, the use of alignment-pooling to improve tracking accuracy and reduce the influence of occlusion, and the use of incremental subspace learning and sparse representation for template update to handle drifting and partial occlusion.This paper proposes a visual tracking method based on a structural local sparse appearance model and an adaptive template update strategy. The method uses a novel alignment-pooling approach to extract spatial and partial information from local image patches, which helps in accurately locating the target and handling occlusion. The sparse representation is combined with incremental subspace learning for template update, allowing the tracker to adapt to appearance changes and reduce the influence of occluded templates. The proposed method is evaluated on challenging benchmark sequences and shows favorable performance compared to several state-of-the-art methods. The method samples overlapped local image patches within the target region and uses a spatial layout to capture both spatial and partial information. The alignment-pooling method is used to compute similarity across local patches, which helps in more accurate target localization and occlusion handling. The dictionary for local sparse coding is generated from dynamic templates, which are updated online using both incremental subspace learning and sparse representation. This update strategy allows the tracker to account for appearance changes of the target and reduces the influence of occluded templates. The proposed method uses a structural local sparse appearance model that captures the structural information of the target object in terms of blocks. This model is able to handle partial occlusion by using sparse coding coefficients with the alignment-pooling method. The method also employs a template update strategy that combines incremental subspace learning and sparse representation to adapt the template to the appearance change of the target and reduce the influence of occluded templates. The method is evaluated on challenging benchmark sequences, including those with illumination variation, partial occlusion, pose variation, background clutter, and scale change. The results show that the proposed method performs favorably against several state-of-the-art tracking methods. The method is efficient and robust, making it suitable for real-world applications. The contributions of this work include the use of sparse codes of local image patches with spatial layout to model the appearance of the target, the use of alignment-pooling to improve tracking accuracy and reduce the influence of occlusion, and the use of incremental subspace learning and sparse representation for template update to handle drifting and partial occlusion.