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). 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. 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. Results indicate that the proposed SPAM-DDWF tracking algorithm performs accurately

effectively

and robustly

especially when the object is occluded by analogues.