Name
Abstract | ||
|---|---|---|
In this paper, we propose a new visual object tracking approach via one-class SVM (OC-SVM), inspired by the fact that OCSVM's support vectors can form a hyper-sphere, whose center can be regarded as a robust object estimation from samples. In the tracking approach, a set of tracking samples are constructed in a predefined searching window of a video frame. And then a threshold strategy is proposed to select examples from the tracking sample set. Selected examples are used to train an OC-SVM model which estimates a hyper-sphere encircling most of the examples. Finally, we locate the center of the hyper sphere as the tracked object in the current frame. Extensive experiments demonstrate the effectiveness and robustness of the proposed approach in complex background. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-22822-3_22 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
oc-svm model,tracked object,robust object estimation,one-class svm,complex background,current frame,tracking approach,new visual object tracking,tracking sample set,video frame,object tracking | Computer vision,Pattern recognition,Computer science,Support vector machine,Robustness (computer science),Video tracking,Artificial intelligence | Conference |
Volume | Issue | ISSN |
6468 LNCS | PART1 | 16113349 |
Citations | PageRank | References |
2 | 0.38 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Li | 1 | 6 | 2.22 |
| Zhenjun Han | 2 | 176 | 16.40 |
| Qixiang Ye | 3 | 913 | 64.51 |
| Jianbin Jiao | 4 | 367 | 32.61 |