Abstract: The reverse joint sparse representation algorithm can make full use of the temporal similarity and spatial continuity in the tracking process. However, tracking drift can be easily incurred under the influence of occlusion and illumination change. Aiming at this problem, we proposed the reverse joint sparse representation tracker (RJST). It can accomplish the reverse joint sparse representation through the reversely local reconstruction of the object template set. Firstly, the object template set was initialized in the first frame, and the candidate images were generated by particle filtering. They were partitioned into blocks, and the reverse joint sparse representation model was constructed. Then, the sparse coding matrix was solved using the alternating direction method of multipliers. The optimal candidate image was acquired by the two-step scoring mechanism. Finally, whether the current object had local occlusion was evaluated according to the similarity score. If there was no occlusion, the object template set was locally updated to eliminate the tracking drift. Experimental results show that the precision and success rate of RJST reached 85.4% and 62.8% on the OTB-2013 benchmark, and 76.8% and 68.6% on the OTB100 benchmark, respectively, and that the speed was 5.76 frames per second, which can effectively boost robustness and eliminate tracking drift. 

Key words: object tracking, reverse joint sparse representation, spatio-temporal information, local template update; alternating direction method of multipliers