OWP: Objectness Weighted Patch Descriptor for Visual Tracking

Bo Jiang, Yuan Zhang, Jin Tang, Bin Luo

Abstract: Visual object tracking is an active research problem and has been widely used in computer vision and pattern recognition area. Existing visual tracking methods usually localize the visual object with a bounding box which are often disturbed by the introduced background information and partial occlusion because of bounding box representation of visual object. To deal with this problem, in this paper, we propose a novel Objectness Weighted Patch (OWP) descriptor for object feature descriptor in visual tracking. The aim of OWP is to assign different objectness weights to the patches of bounding box to reduce the influences of background information and partial occlusion. We propose to compute the objectness weights of patches in OWP by integrating multiple cues (background, foreground and local spatial consistency) together in a general optimization model. Also, the proposed model has a simple closed-form solution and thus can be computed efficiently. We incorporate our OWP into structured SVM tracking framework and provide a new robust tracking method. Extensive experiments on two standard benchmark datasets OTB100 and Temple-Color demonstrate the effectiveness and benefits of the proposed tracking method.