The traditional subspaces based visual trackers well solved appearance changes and occlusions.However

they were weakly robust for complex background and prone to model drifting.To deal with these two problems

this paper enlarges reconstruction errors of the background samples and uses L1-norm loss function to establish an online robust discrimination dictionary learning model.Then an online robust discrimination dictionary learning algorithm for template updating in visual tracking is designed via the block coordinate descent (BCD).Finally

robust visual tracking is achieved with the proposed template updating method in particle filter framework.The experimental results show that the proposed method has better performance in robustness and accuracy than the state-of-the-art trackers such as IVT(Incremental Visual Tracking)

L1APG(L1-tracker using Accelerated Proximal Gradient)

ONNDL(Online Non-Negative Dictionary Learning) and PCOM(Probability Continuous Outlier Model).