scholarly journals Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

2014 ◽  
Vol 111 (2) ◽  
pp. 171-190 ◽  
Author(s):  
Tianzhu Zhang ◽  
Si Liu ◽  
Narendra Ahuja ◽  
Ming-Hsuan Yang ◽  
Bernard Ghanem
Keyword(s):  
Visual Tracking ◽  
Low Rank ◽  
Sparse Learning

Low-Rank Sparse Learning for Robust Visual Tracking

2012 ◽  
pp. 470-484 ◽  
Author(s):  
Tianzhu Zhang ◽  
Bernard Ghanem ◽  
Si Liu ◽  
Narendra Ahuja
Keyword(s):  
Visual Tracking ◽  
Low Rank ◽  
Sparse Learning

Temporal Restricted Visual Tracking Via Reverse-Low-Rank Sparse Learning

2016 ◽  
pp. 1-14 ◽  
Author(s):  
Yehui Yang ◽  
Wenrui Hu ◽  
Yuan Xie ◽  
Wensheng Zhang ◽  
Tianzhu Zhang
Keyword(s):  
Visual Tracking ◽  
Low Rank ◽  
Sparse Learning

scholarly journals Robust Object Tracking via Reverse Low-Rank Sparse Learning and Fractional-Order Variation Regularization

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Dan Tian ◽  
Guoshan Zhang ◽  
Shouyu Zang
Keyword(s):  
Object Tracking ◽  
Fractional Order ◽  
Low Rank ◽  
Sparse Learning ◽  
Target Template ◽  
Order Variation ◽  
Sparse Coefficient ◽  
Fast Motion ◽  
The Difference ◽  
Representation Method

Object tracking based on low-rank sparse learning usually makes the drift phenomenon occur when the target faces severe occlusion and fast motion. In this paper, we propose a novel tracking algorithm via reverse low-rank sparse learning and fractional-order variation regularization. Firstly, we utilize convex low-rank constraint to force the appearance similarity of the candidate particles, so as to prune the irrelevant particles. Secondly, fractional-order variation is introduced to constrain the sparse coefficient difference in the bounded variation space, which allows the difference between consecutive frames to exist, so as to adapt object fast motion. Meanwhile, fractional-order regularization can restrain severe occlusion by considering more adjacent frames information. Thirdly, we employ an inverse sparse representation method to model the relationship between target candidates and target template, which can reduce the computation complexity for online tracking. Finally, an online updating scheme based on alternating iteration is proposed for tracking computation. Experiments on benchmark sequences show that our algorithm outperforms several state-of-the-art methods, especially exhibiting better adaptability for fast motion and severe occlusion.

Robust Visual Tracking via Binocular Consistent Sparse Learning

2017 ◽  
Vol 46 (2) ◽  
pp. 627-642 ◽  
Author(s):  
Ziang Ma ◽  
Zhiyu Xiang
Keyword(s):  
Visual Tracking ◽  
Sparse Learning

Robust Visual Tracking via Structured Multi-Task Sparse Learning

2012 ◽  
Vol 101 (2) ◽  
pp. 367-383 ◽  
Author(s):  
Tianzhu Zhang ◽  
Bernard Ghanem ◽  
Si Liu ◽  
Narendra Ahuja
Keyword(s):  
Visual Tracking ◽  
Sparse Learning

Robust visual tracking via L 0 regularized local low-rank feature learning

2015 ◽  
Vol 24 (3) ◽  
pp. 033012 ◽  
Author(s):  
Risheng Liu ◽  
Shanshan Bai ◽  
Zhixun Su ◽  
Changcheng Zhang ◽  
Chunhai Sun
Keyword(s):  
Visual Tracking ◽  
Feature Learning ◽  
Low Rank
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