Locality-Constrained Collaborative Model for Robust Visual Tracking

This paper presents a novel discriminative, generative, and collaborative appearance model for robust object tracking. In contrast to existing methods, we use different appearance manifolds to represent the target in the discriminative and generative appearance models and propose a novel collaborative scheme to combine these two components. In particular: 1) for the discriminative component, we develop a graph regularized discriminant analysis (GRDA) algorithm that can find a projection to more effectively distinguish the target from the background; 2) for the generative component, we introduce a simple yet effective coding method for object representation. The method involves no optimization, and thus better efficiency can be achieved; and 3) for the collaborative model, we apply GRDA again to find a subspace for discriminating the likelihood features (generated from the discriminative and generative appearance models) and use the nearest neighbor criterion to determine the final likelihood. Besides, all the components are online updated so that our tracker can deal with appearance changes effectively. The experimental results over 23 challenging image sequences demonstrate that the proposed algorithm achieves better performance compared with other state-of-the-art methods.