Graph based visual object tracking
Zhou Guanling, Wang Yu-ping, Dong Nanping · 2009
Object tracking is viewed as a two-class ldquoone-versus-restrdquo classification problem, in which the sample distribution of the target is approximately Gaussian while the background samples are often multi-modal. Based on these special properties, we model the visual appearance via graph approach, which is a semi-supervised approach. The topology structure of graph is carefully designed to reflect the properties of the sample's distribution. The confidence of sample's label is computed via random walk with restart (RWR). The primary advantage of our algorithm is that it keeps the appearance of object via semi-supervised method. Experimental results demonstrate that, compared with two state of the art methods, the proposed tracking algorithm is more effective, especially in dynamically changing and clutter scenes.