Name
Abstract | ||
|---|---|---|
Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3837/tiis.2019.01.018 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
Visual tracking,correlation filter,complementary learning,adaptive weight,collaborative model | Computer vision,Correlation filter,Computer science,Collaborative model,Eye tracking,Artificial intelligence,Distributed computing | Journal |
Volume | Issue | ISSN |
13 | 1 | 1976-7277 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benxuan Wang | 1 | 0 | 0.68 |
| Jun Kong | 2 | 111 | 18.94 |
| Min Jiang | 3 | 39 | 13.65 |
| Jianyu Shen | 4 | 0 | 0.34 |
| Tianshan Liu | 5 | 9 | 4.27 |
| Xiaofeng Gu | 6 | 113 | 14.72 |