A robust correlation filter tracking method based on adaptive model updating

2021 ◽  
Author(s):  
Lin Zhang ◽  
Xingzhong Xiong ◽  
Xin Zeng ◽  
Ya Dong
Keyword(s):  
Model Updating ◽  
Correlation Filter ◽  
Adaptive Model ◽  
Tracking Method

scholarly journals AUV Tunnel Tracking Method Based on Adaptive Model Predictive Control

2018 ◽  
Vol 428 ◽  
pp. 012070 ◽  
Author(s):  
Chenkai Jia ◽  
Zhou Chen ◽  
Guocheng Zhang ◽  
Lei Shi ◽  
Yushan Sun ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Adaptive Model ◽  
Tracking Method ◽  
Adaptive Model Predictive Control

Fast motion tracking based on moth-flame optimization and kernel correlation filter

2020 ◽  
Vol 39 (3) ◽  
pp. 3825-3837
Author(s):  
Yibin Chen ◽  
Guohao Nie ◽  
Huanlong Zhang ◽  
Yuxing Feng ◽  
Guanglu Yang
Keyword(s):  
Motion Tracking ◽  
State Of The Art ◽  
Candidate Region ◽  
Correlation Filter ◽  
Data Sets ◽  
Search Window ◽  
Tracking Method ◽  
Fast Motion ◽  
Kernel Correlation

Kernel Correlation Filter (KCF) tracker has shown great potential on precision, robustness and efficiency. However, the candidate region used to train the correlation filter is fixed, so tracking is difficult when the target escapes from the search window due to fast motion. In this paper, an improved KCF is put forward for long-term tracking. At first, the moth-flame optimization (MFO) algorithm is introduced into tracking to search for lost target. Then, the candidate sample strategy of KCF tracking method is adjusted by MFO algorithm to make it has the capability of fast motion tracking. Finally, we use the conservative learning correlation filter to judge the moving state of the target, and combine the improved KCF tracker to form a unified tracking framework. The proposed algorithm is tested on a self-made dataset benchmark. Moreover, our method obtains scores for both the distance precision plot (0.891 and 0.842) and overlap success plots (0.631 and 0.601) on the OTB-2013 and OTB-2015 data sets, respectively. The results demonstrate the feasibility and effectiveness compared with the state-of-the-art methods, especially in dealing with fast or uncertain motion.

scholarly journals Adaptive Model Update Strategy for Correlation Filter Trackers

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 151493-151505
Author(s):  
Zhuang He ◽  
Qi Li ◽  
Meng Chang ◽  
Huajun Feng ◽  
Zhihai Xu
Keyword(s):  
Correlation Filter ◽  
Adaptive Model ◽  
Model Update ◽  
Update Strategy
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