Abstract
| Original language | English |
|---|---|
| Title of host publication | 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) |
| Place of Publication | United States |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| ISBN (Electronic) | 9781538628393 |
| DOIs | |
| Publication status | Published - 2017 |
| Event | 2017 International Conference on Digital Image Computing: Techniques and Applications - Sydney, Australia Duration: 29 Nov 2017 → 1 Dec 2017 |
Conference
| Conference | 2017 International Conference on Digital Image Computing: Techniques and Applications |
|---|---|
| Abbreviated title | DICTA |
| Country | Australia |
| City | Sydney |
| Period | 29/11/17 → 1/12/17 |
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Learning Variance Kernelized Correlation Filters for Robust Visual Object Tracking. / Liu, Chenghuan; Huynh, Du; Reynolds, Mark.
2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). United States : IEEE, Institute of Electrical and Electronics Engineers, 2017.Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - Learning Variance Kernelized Correlation Filters for Robust Visual Object Tracking
AU - Liu, Chenghuan
AU - Huynh, Du
AU - Reynolds, Mark
PY - 2017
Y1 - 2017
N2 - Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable features and kernel functions incorporated, can give very promising tracking results. In this paper, we propose to learn discriminative correlation filters that incorporate information from the variances of the target's appearance features. We have evaluated our filters against several recent tracking methods on the OTB benchmark dataset. Our results show that the additional feature variances help to improve the robustness of the correlation filters in complex scenes.
AB - Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable features and kernel functions incorporated, can give very promising tracking results. In this paper, we propose to learn discriminative correlation filters that incorporate information from the variances of the target's appearance features. We have evaluated our filters against several recent tracking methods on the OTB benchmark dataset. Our results show that the additional feature variances help to improve the robustness of the correlation filters in complex scenes.
U2 - 10.1109/DICTA.2017.8227458
DO - 10.1109/DICTA.2017.8227458
M3 - Conference paper
BT - 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
ER -