Visual Tracking via Random Walks on Graph Model
将视觉跟踪问题转化为图模型上的随机游走，通过遍历马尔可夫链全局搜索候选节点，并用吸收马尔可夫链建模帧间时间一致性，最终结合外观相似性和空间布局生成置信图，在挑战性序列上优于现有方法。
In this paper, we formulate visual tracking as random walks on graph models with nodes representing superpixels and edges denoting relationships between superpixels. We integrate two novel graphs with the theory of Markov random walks, resulting in two Markov chains. First, an ergodic Markov chain is enforced to globally search for the candidate nodes with similar features to the template nodes. Second, an absorbing Markov chain is utilized to model the temporal coherence between consecutive frames. The final confidence map is generated by a structural model which combines both appearance similarity measurement derived by the random walks and internal spatial layout demonstrated by different target parts. The effectiveness of the proposed Markov chains as well as the structural model is evaluated both qualitatively and quantitatively. Experimental results on challenging sequences show that the proposed tracking algorithm performs favorably against state-of-the-art methods.