Multi-Model and Multi-Expert Correlation Filter for High-Speed Tracking
Tracking algorithm based on correlation filter have been extensively investigated due to their powerful performance in benchmark datasets and competitions. However, the periodic assumption has contributed boundary effects and the complex scenarios will give rise to model drift, which have an extreme...
Main Authors: | Mengquan Liang, Xuedong Wu, Yaonan Wang, Zhiyu Zhu, Baiheng Cao, Jie Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9389799/ |
Similar Items
-
Robust Visual Tracking via Multiple Experts With Correlation Filters
by: Lei Zhang, et al.
Published: (2019-01-01) -
LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation
by: Yixuan Zhou, et al.
Published: (2020-11-01) -
Online Multi-Object Tracking via Combining Discriminative Correlation Filters With Making Decision
by: Chenglong Wu, et al.
Published: (2018-01-01) -
An Anti-Drift Background-Aware Correlation Filter for Visual Tracking in Complex Scenes
by: Shanshan Luo, et al.
Published: (2019-01-01) -
Robust Measurement-Driven Cardinality Balance Multi-Target Multi-Bernoulli Filter
by: Biao Yang, et al.
Published: (2021-08-01)