Robust Visual Tracking Algorithm Based on Structural Multi-scale Features Adaptive Fusion in Co-training

2016 ◽  
Author(s):  
Chao Zheng ◽  
Wei Jin ◽  
Fang Fang ◽  
Chong Tang ◽  
Yongshun Ling
Keyword(s):  
Visual Tracking ◽  
Tracking Algorithm ◽  
Multi Scale ◽  
Adaptive Fusion

Multi-Scale Correlation Filtering Visual Tracking Algorithm Combined with Target Detection

2019 ◽  
Vol 39 (1) ◽  
pp. 0115004
Author(s):  
王红雨 Wang Hongyu ◽  
汪梁 Wang Liang ◽  
尹午荣 Yin Wurong ◽  
胡江颢 Hu Jianghao ◽  
乔文超 Qiao Wenchao
Keyword(s):  
Target Detection ◽  
Visual Tracking ◽  
Tracking Algorithm ◽  
Multi Scale ◽  
Correlation Filtering

Robust visual tracking via identifying multi-scale patches

2018 ◽  
Vol 78 (11) ◽  
pp. 14195-14230 ◽  
Author(s):  
Yun Liang ◽  
Ke Li ◽  
Jian Zhang ◽  
Meihua Wang ◽  
Chen Lin
Keyword(s):  
Visual Tracking ◽  
Multi Scale

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.

Visual tracking algorithm based on the sparse-regularized subspace

2018 ◽  
Vol 26 (4) ◽  
pp. 989-997
Author(s):  
陈典兵 CHEN Dian-bing ◽  
朱明 ZHU Ming ◽  
王慧利 WANG Hui-li ◽  
杨航 YANG Hang
Keyword(s):  
Visual Tracking ◽  
Tracking Algorithm

Adaptive multi-scale color feature target tracking algorithm

2019 ◽  
Vol 34 (3) ◽  
pp. 291-301
Author(s):  
李晓云 LI Xiao-yun ◽  
何秋生 HE Qiu-sheng ◽  
张卫峰 ZHANG Wei-feng ◽  
梁慧慧 LIANG Hui-hui ◽  
陈 伟 CHEN Wei
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Color Feature ◽  
Multi Scale ◽  
Feature Target
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