目的 当前大多数基于稀疏表示的跟踪方法只考虑全局特征或局部特征的最小重构误差，没有充分利用稀疏编码系数，或者忽略了字典判别性的作用，尤其当目标被相似物遮挡时，往往会导致跟踪目标丢失。针对上述问题，提出一种新的基于判别式字典和加权局部特征的稀疏外观模型(SPAM-DDWF)跟踪算法。方法 首先利用Fisher准则学习判别式字典，对提取的局部特征进行结构性分析来区分目标和背景，其次，提出一种新的基于加权的相似性度量方法来处理遮挡问题，从而提高跟踪的精确度。此外，基于重构系数的权重更新策略，使算法能更好地适应跟踪目标的外观变化，并降低了遮挡发生时跟踪漂移的概率。结果 在多个基准图像序列上，与多种流行方法对比，本文算法在光照变化、复杂背景、遮挡等场景中保持较高的跟踪成功率与较低的漂移误差。平均成功率和漂移误差分别为76.8%和3.7。结论 实验结果表明，本文算法具有较好的有效性和鲁棒性，尤其在目标被相似物遮挡的情况下，也能较准确地跟踪到目标。

Objective Most trackers base on sparse representation-based trackers consider only the minimal reconstruction error of the holistic representation or local features without fully utilizing sparse coefficients or ignoring the discriminant of dictionaries. Thus, these trackers share a high possibility of failure when a similar object or occlusion is present in the scene. Thus, this study proposes a novel tracker based on sparse appearance model with discriminative dictionary and weighted features (SPAM-DDWF).Method First, the proposed algorithm introduces the Fisher discriminative dictionary. We then use the discriminative dictionary to distinguish the target from the background. The weighted alignment-pooling based similarity measurement is proposed to locate the target accurately and handle the occlusionfinely. Furthermore, we employ a reconstruction error-basedupdate strategy of the weights of the sparse coefficients. This strategy adapts to changes in the appearance of the targetand reduces the possibility of a drifting problem when occlusion occurs.Result Compared with several state-of-the-art trackers on most benchmark sequences, the proposed tracker maintains a higher success rate and lower drifting error in scenes with illumination changes, complex background, and occlusion.The proposed tracker reaches a 76.8% average success rate and 3.7% success in decreasing the drifting error.Conclusion Results indicate that the proposed SPAM-DDWF tracking algorithm performs accurately, effectively, and robustly, especially when the object is occluded by analogues.