Name
Abstract | ||
|---|---|---|
In this paper, we present a novel online visual tracking method based on linear representation. First, we present a novel probability continuous outlier model (PCOM) to depict the continuous outliers that occur in the linear representation model. In the proposed model, the element of the noisy observation sample can be either represented by a PCA subspace with small Guassian noise or treated as an arbitrary value with a uniform prior, in which the spatial consistency prior is exploited by using a binary Markov random field model. Then, we derive the objective function of the PCOM method, the solution of which can be iteratively obtained by the outlier-free least squares and standard max-flow/min-cut steps. Finally, based on the proposed PCOM method, we design an effective observation likelihood function and a simple update scheme for visual tracking. Both qualitative and quantitative evaluations demonstrate that our tracker achieves very favorable performance in terms of both accuracy and speed. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CVPR.2014.445 | CVPR |
Keywords | DocType | ISSN |
noisy observation sample element,image representation,visual tracking,online visual tracking method,random processes,observation likelihood function,pcom method,linear representation model,objective function,probability continuous outlier model,markov processes,spatial consistency prior,outlier-free least squares,minimax techniques,least squares approximations,linear representation,max-flow-min-cut steps,object tracking,pca subspace,binary markov random field model,visual tracking, outlier model, linear representation,small guassian noise,outlier model,principal component analysis,gaussian noise,iterative methods,probability,mathematical model,vectors,visualization | Conference | 1063-6919 |
Citations | PageRank | References |
53 | 1.32 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Wang | 1 | 326 | 14.06 |
| Huchuan Lu | 2 | 4827 | 186.26 |