scholarly journals Kernel correlation filter tracking strategy based on adaptive fusion response map

2021 ◽  
Author(s):  
Chunbo Xiu ◽  
Yunfei Ma
Keyword(s):  
Correlation Filter ◽  
Adaptive Fusion ◽  
Kernel Correlation ◽  
Fusion Response ◽  
Tracking Strategy

scholarly journals Correlation Tracking via Self-Adaptive Fusion of Multiple Features

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 241 ◽  
Author(s):  
Zhi Chen ◽  
Peizhong Liu ◽  
Yongzhao Du ◽  
Yanmin Luo ◽  
Wancheng Zhang
Keyword(s):  
State Of The Art ◽  
Correlation Filter ◽  
Multiple Features ◽  
Multi Scale ◽  
Tracking Algorithms ◽  
Model Update ◽  
Adaptive Fusion ◽  
Tracking Model ◽  
Update Strategy ◽  
Self Adaptive

Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods.

Infrared target tracking with correlation filter based on adaptive fusion of responses

2019 ◽  
Vol 48 (6) ◽  
pp. 626003
Author(s):  
房胜男 Fang Shengnan ◽  
谷小婧 Gu Xiaojing ◽  
顾幸生 Gu Xingsheng
Keyword(s):  
Target Tracking ◽  
Correlation Filter ◽  
Adaptive Fusion

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.

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