Robust Visual Tracking via Smooth Manifold Kernel Sparse Learning

摘要

Various sparse-representation-based tracking meth-ods have been proposed to tackle visual tracking problems, and most of them use simple intensity feature as the observation of target object. Moreover, most of them only take into account either global or local image representation and only exploit the underlying relationship among target candidates in a single frame. All of these may make their appearance models less robust to deal with complex scenes. To overcome these problems, we propose a smooth manifold kernel sparse tracker under the framework of particle filter. The proposed method characterizes targets and candidates with region covariance matrix descriptors, and constructs object tracking as a kernel sparse learning model based on symmetric positive-definite (SPD) manifolds. The spatial-temporal interdependencies among candidates and global-local representations of candidates are jointly considered and unified via the kernel sparse learning model. Moreover, in order to make the model more robust, the detection of outlier tasks is also taken into account. To handle the variation of object appearance, we develop a robust and efficient online dictionary learning algorithm on SPD manifolds. Extensive experiments on multiple benchmark datasets demonstrate that our tracker performs favorably against state-of-the-art trackers.