Name
Abstract | ||
|---|---|---|
In this paper, a novel and robust method which exploits the spatiotemporal context for orderless and blurred visual tracking is presented. This lets the tracker adapt to both rigid and deformable objects on-line even if the image is blurred. We observe that a RGB vector of an image which is resized into a small fixed size can keep enough useful information. Based on this observation and computational reasons, we propose to resize the windows of both template and candidate target images into 2×2 and use Euclidean Distance to compute the similarity between these two RGB image vectors for the preliminary screening. We then apply spatio-temporal context based on Bayesian framework to further compute a confidence map for obtaining the best target location. Experimental results on challenging video sequences in MATLAB without code optimization show the proposed tracking method outperforms eight state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-14445-0_3 | MMM |
Field | DocType | Citations |
Program optimization,Computer vision,MATLAB,Pattern recognition,Context based,Computer science,Euclidean distance,Eye tracking,Artificial intelligence,RGB color model,Temporal context,Bayesian probability | Conference | 2 |
PageRank | References | Authors |
0.36 | 19 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manna Dai | 1 | 9 | 2.48 |
| Peijie Lin | 2 | 4 | 4.12 |
| Lijun Wu | 3 | 124 | 21.21 |
| Zhicong Chen | 4 | 6 | 4.20 |
| Songlin Lai | 5 | 2 | 0.36 |
| Jie Zhang | 6 | 47 | 15.01 |
| Shuying Cheng | 7 | 7 | 4.52 |
| Xiangjian He | 8 | 932 | 132.03 |