Name
Title | ||
|---|---|---|
Learning reliable-spatial and spatial-variation regularization correlation filters for visual tracking |
Abstract | ||
|---|---|---|
Single-object tracking is a significant and challenging computer vision problem. Recently, discriminative correlation filters (DCF) have shown excellent performance. But there is a theoretical defects that the boundary effect, caused by the periodic assumption of training samples, greatly limit the tracking performance. Spatially regularized DCF (SRDCF) introduces a spatial regularization to penalize the filter coefficients depending on their spatial location, which improves the tracking performance a lot. However, this simple regularization strategy implements unequal penalties for the target area filter coefficients, which makes the filter learn a distorted object appearance model. In this paper, a novel spatial regularization strategy is proposed, utilizing a reliability map to approximate the target area and to keep the penalty coefficients of relevant region consistent. Besides, we introduce a spatial variation regularization component that the second-order difference of the filter, which smooths changes of filter coefficients to prevent the filter over-fitting current frame. Furthermore, an efficient optimization algorithm called alternating direction method of multipliers (ADMM) is developed. Comprehensive experiments are performed on three benchmark datasets: OTB-2013, OTB-2015 and TempleColor-128, and our algorithm achieves a more favorable performance than several state-of-the-art methods. Compared with SRDCF, our approach obtains an absolute gain of 6.6% and 5.1% in mean distance precision on OTB-2013 and OTB-2015, respectively. Our approach runs in real-time on a CPU. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.imavis.2020.103869 | Image and Vision Computing |
Keywords | Field | DocType |
Correlation filters,Visual tracking,Spatial regularization | Pattern recognition,Active appearance model,Absolute gain,Regularization (mathematics),Eye tracking,Artificial intelligence,Spatial variability,Discriminative model,Periodic graph (geometry),Mathematics,Filter design | Journal |
Volume | ISSN | Citations |
94 | 0262-8856 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hengcheng Fu | 1 | 0 | 0.68 |
| Yihong Zhang | 2 | 9 | 10.65 |
| Wuneng Zhou | 3 | 467 | 53.74 |
| Xiaofeng Wang | 4 | 1 | 1.03 |
| Huanlong Zhang | 5 | 36 | 13.12 |