Name
Abstract | ||
|---|---|---|
Matching visual appearances of the target object over consecutive frames is a critical step in visual tracking. The accuracy performance of a practical tracking system highly depends on the similarity metric used for visual matching. Recent attempts to integrate discriminative metric learned by sequential visual data (instead of a predefined metric) in visual tracking have demonstrated more robust and accurate results. However, a global similarity metric is often suboptimal for visual matching when the target object experiences large appearance variation or occlusion. To address this issue, we propose in this paper a spatially weighted similarity fusion (SWSF) method for robust visual tracking. In our SWSF, a part-based model is employed as the object representation, and the local similarity metric and spatially regularized weights are jointly learned in a coherent process, such that the total matching accuracy between visual target and candidates can be effectively enhanced. Empirically, we evaluate our proposed tracker on various challenging sequences against several state-of-the-art methods, and the results demonstrate that our method can achieve competitive or better tracking performance in various challenging tracking scenarios. A spatially weighted similarity fusion scheme for more robust visual trackingLocal similarity metric and its weights are jointly learned in a coherent online process.Evaluations show the robustness to partial occlusion with high matching accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.imavis.2016.11.016 | Image Vision Comput. |
Keywords | Field | DocType |
Visual tracking,Part-based representation,Metric learning,Local similarity,Spatially regularized | Computer vision,Pattern recognition,Tracking system,Robustness (computer science),Eye tracking,Visual matching,Artificial intelligence,Fusion scheme,Discriminative model,Mathematics | Journal |
Volume | Issue | ISSN |
60 | C | 0262-8856 |
Citations | PageRank | References |
0 | 0.34 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiuzhuang Zhou | 1 | 380 | 20.26 |
| Qirun Huo | 2 | 2 | 1.38 |
| Yuanyuan Shang | 3 | 210 | 16.83 |
| Min Xu | 4 | 18 | 4.41 |
| Hui Ding | 5 | 0 | 1.69 |