Visual tracking via efficient kernel discriminant subspace learning

Robustly tracking moving objects in video sequences is one of the key problems in computer vision. In this paper we introduce a computationally efficient nonlinear kernel learning strategy to find a discriminative model which distinguishes the tracked object from the background. Principal component analysis and linear discriminant analysis have been applied to this problem with some success. These techniques are limited, however, by the fact that they are capable only of identifying linear subspaces within the data. Kernel based methods, in contrast, are able to extract nonlinear subspaces, and thus represent more complex characteristics of the tracked object and background. This is a particular advantage when tracking deformable objects and where appearance changes due to the unstable illumination and pose occur. An efficient approximation to kernel discriminant analysis using QR decomposition proposed by Xiong et al. (2004) makes possible real-time updating of the optimal nonlinear subspace. We present a tracking method based on this result and show promising experimental results on real videos undergoing large pose and illumination changes.