Context multi-task visual object tracking via guided filter

Abstract

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. The target is treated as the guidance and the sampling examples are filtered depending on the similarity between the target and each input. The edge preserving smoothing property of the guided filter is a key factor for object tracking. First, valuable candidates can be selected and the inaccurate candidates become blurry. Therefore, the guided filter can help to distinguish the target from numerous candidates easily. Second, partial occluded target can be recovered by the filtering process via the guidance image. Thus, the drifting problem can be alleviated. 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 yields a sequence of closed form updates. We test our tracker on challenging video sequences that involve drastic illumination changes, large pose variations, and heavy occlusions. Experimental results show that our tracker consistently outperforms state-of-the-art trackers.