Name
Abstract | ||
|---|---|---|
Visual tracking is a challenging task since it involves developing an effective appearance model to deal with numerous factors. In this paper, we propose a robust object tracking algorithm based on a collaborative model with adaptive selection scheme. Specifically, based on the discriminative features selected from the feature selection scheme, we develop a sparse discriminative model (SDM) by introducing a confidence measure strategy. In addition, we present a sparse generative model (SGM) by combining ℓ1 regularization with PCA reconstruction. In contrast to existing hybrid generative discriminative tracking algorithms, we propose a novel adaptive selection scheme based on the Euclidean distance as the joint mechanism, which helps to construct a more reasonable likelihood function for our collaborative model. Experimental results on several challenging image sequences demonstrate that the proposed tracking algorithm leads to a more favorable performance compared with the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s13042-017-0709-1 | Int. J. Machine Learning & Cybernetics |
Keywords | Field | DocType |
Visual tracking, Collaborative model, Adaptive selection scheme, Sparse representation | Likelihood function,Feature selection,Pattern recognition,Computer science,Sparse approximation,Euclidean distance,Active appearance model,Eye tracking,Artificial intelligence,Discriminative model,Machine learning,Generative model | Journal |
Volume | Issue | ISSN |
10 | 2 | 1868-808X |
Citations | PageRank | References |
2 | 0.38 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianshan Liu | 1 | 9 | 4.27 |
| Jun Kong | 2 | 111 | 18.94 |
| Min Jiang | 3 | 39 | 13.65 |
| Chenhua Liu | 4 | 2 | 0.38 |
| Xiaofeng Gu | 5 | 113 | 14.72 |
| Xiaofeng Wang | 6 | 4 | 10.54 |