Name
Abstract | ||
|---|---|---|
In this article, a novel algorithm - CamShift guided particle filter (CAMSGPF) - is proposed for tracking object in video sequence. CamShift is incorporated into the probabilistic framework of particle filter as an optimization scheme for proposal distribution. Meanwhile, in the context of particle filter, the scale adaptation of CamShift is improved and the computation complexity is reduced. It is demonstrated through several real tracking tasks that the new method performs better than baseline trackers in both tracking robustness and computational efficiency. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.patrec.2008.10.017 | Pattern Recognition Letters |
Keywords | Field | DocType |
sir,camshift guided particle filter,real tracking task,visual tracking algorithm,computational efficiency,sequential importance resampling,msepf,optimization scheme,visual tracking,camshift,5.013,mean shift embedded particle filter,tracking robustness,probabilistic framework,particle filter,baseline tracker,computation complexity,8.002,camsgpf,new method,pf,novel algorithm,information processing,mean shift,particle filters,robustness,probability distribution | Computer vision,Information processing,Computer science,Particle filter,Robustness (computer science),Probability distribution,Eye tracking,Redundancy (engineering),Artificial intelligence,Mean-shift | Journal |
Volume | Issue | ISSN |
30 | 4 | Pattern Recognition Letters |
Citations | PageRank | References |
34 | 1.53 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaowen Wang | 1 | 1063 | 40.64 |
| Xiaokang Yang | 2 | 3581 | 238.09 |
| Yi Xu | 3 | 1757 | 177.61 |
| Yu Song | 4 | 356 | 52.74 |