In this paper, we formulate particle filter based tracking as a multi-task sparse learning problem that exploits context information. The target and context information which modeled as linear combinations of principal component analysis (PCA) basis is formed as dictionary templates. We treat the dictionary templates as the guidance and the incoming candidates are filtered depending on the similarity between the guidance image and each input. The guided filter can help to distinguish the target from numerous candidates via context information. Then multi-task sparse learning is employed to learn the target and context information. The proposed learning problem is efficiently solved using an alternating direction method of multipliers (ADMM) method that yield a sequence of closed form updates. We test our tracker on challenging benchmark sequences that involve drastic illumination changes, large pose variations, and heavy occlusion. Experimental results show that our tracker consistently outperforms state-of-the-art trackers.