Author
Listed:
- Zhongyang Wang
(School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)
- Feng Liu
(School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)
- Lizhen Deng
(School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)
Abstract
Recently, discriminative correlation filters (DCF) have been successfully applied for visual tracking. However, traditional DCF trackers tend to separately solve boundary effect and temporal degradation problems in the tracking process. In this paper, a variational online learning correlation filter (VOLCF) is proposed for visual tracking to improve the robustness and accuracy of the tracking process. Unlike previous methods, which use only first-order temporal constraints, this approach leads to overfitting and filter degradation. First, beyond the standard filter training requirement, our proposed VOLCF method introduces a model confidence term, which leverages the temporal information of adjacent frames during filter training. Second, to ensure the consistency of the temporal and spatial characteristics of the video sequence, the model introduces Kullback–Leibler (KL) divergence to obtain the second-order information of the filter. In contrast to traditional target tracking models that rely solely on first-order feature information, this approach facilitates the acquisition of a generalized connection between the previous and current filters. As a result, it incorporates joint-regulated filter updating. Through quantitative and qualitative analyses of the experiment, it proves that the VOLCF model has excellent tracking performance.
Suggested Citation
Zhongyang Wang & Feng Liu & Lizhen Deng, 2024.
"Variational Online Learning Correlation Filter for Visual Tracking,"
Mathematics, MDPI, vol. 12(12), pages 1-22, June.
Handle:
RePEc:gam:jmathe:v:12:y:2024:i:12:p:1818-:d:1412909
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