Abstract: Target tracking algorithm based on sparse coding has good performance for solving the target occlusion problem.However,when the foregrounds of targets were disturbed by the background clutter and illumination,the tracking performance would be seriously decline.We therefore proposed a novel visual tracking algorithm based on dictionary learning and template updating strategy.The background template set was considered and added in the constructed dictionary,and the low dimensional dictionary and target-background classifier were trained simultaneously by using the label consistent K-SVD dictionary learning mechanism.In the sparse coding stage,the particle filter technique was employed,and the overall likelihood of each particle was calculated by using the classification results and the target candidate histogram.Finally,the dictionary,classifier and target histogram were updated by the dictionary learning method.Numerous experiments on various challenging videos show that the proposed algorithm has better tracking performance than some benchmark methods in the scenarios with the interference of occlusion,background clutter,illumination change,target rotation and scale change.