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Recently, weighted patch representation has been widely studied foralleviating the impact of background information included in bounding box toimprove visual tracking results. However, existing weighted patchrepresentation models generally exploit spatial structure information amongpatches in each frame separately which ignore (1) unary featureof each patchand (2) temporal correlation among patches in different frames. To address thisproblem, we propose a novel unified temporal coherence and graph optimizedranking model for weighted patch representation in visual tracking problem.There are three main contributions of this paper. First, we propose to employ aflexible graph ranking for patch weight computation which exploits bothstructure information among patches and unary feature of each patchsimultaneously. Second, we propose a new more discriminative ranking model byfurther considering the temporal correlation among patches in different frames.Third, a neighborhood preserved, low-rank graph is learned and incorporated tobuild a unified optimized ranking model. Experiments on two benchmark datasetsshow the benefits of our model.

Temporal Coherent and Graph Optimized Manifold Ranking for Visual Tracking