Real-time visual tracking using ℓ2 norm regularization based collaborative representation

Recently, sparse representation based visual tracking have been attracting increasing interests. Although reported desired performance, whether the sparse representation constrain is really useful is not clear. In addition, the high computation complexity also limits their usage in real-time applications. In this paper, we proposed a real-time visual tracking framework using ℓ2 norm regularization based collaborative representation. Our framework represents any target candidate using a set of target templates and a set of background templates respectively, then combines their reconstruction errors to track the target accurately. By constraining ℓ2 norm regularization on the representation coefficients, the coefficients can be solved analytically, which makes the proposed method run in real-time. The experimental results demonstrate that the proposed approach outperforms several state-of-the-art trackers.