Occlusion Detection via Structured Sparse Learning for Robust Object Tracking

2014 ◽  
pp. 93-112
Author(s):  
Tianzhu Zhang ◽  
Bernard Ghanem ◽  
Changsheng Xu ◽  
Narendra Ahuja
Keyword(s):  
Object Tracking ◽  
Sparse Learning ◽  
Occlusion Detection ◽  
Structured Sparse Learning

Online structured sparse learning with labeled information for robust object tracking

2017 ◽  
Vol 26 (1) ◽  
pp. 013007 ◽  
Author(s):  
Baojie Fan ◽  
Yang Cong ◽  
Yandong Tang
Keyword(s):  
Object Tracking ◽  
Sparse Learning ◽  
Structured Sparse Learning

Anti-Occlusion Algorithm for Object Tracking Based on Multi-Feature Fusion

2014 ◽  
Vol 610 ◽  
pp. 393-400
Author(s):  
Jie Cao ◽  
Xuan Liang
Keyword(s):  
Object Tracking ◽  
Feature Fusion ◽  
Experimental Results ◽  
Occlusion Detection ◽  
Fusion Algorithm ◽  
Fusion Strategy ◽  
Complex Background ◽  
Complex Scenes

Complex background, especially when the object is similar to the background in color or the target gets blocked, can easily lead to tracking failure. Therefore, a fusion algorithm based on features confidence and similarity was proposed, it can adaptively adjust fusion strategy when occlusion occurs. And this confidence is used among occlusion detection, to overcome the problem of inaccurate occlusion determination when blocked by analogue. The experimental results show that the proposed algorithm is more robust in the case of the cover, but also has good performance under other complex scenes.

scholarly journals Multimodal Neuroimaging Predictors for Cognitive Performance Using Structured Sparse Learning

2012 ◽  
pp. 1-17 ◽  
Author(s):  
Jingwen Yan ◽  
Shannon L. Risacher ◽  
Sungeun Kim ◽  
Jacqueline C. Simon ◽  
Taiyong Li ◽  
...  
Keyword(s):  
Cognitive Performance ◽  
Sparse Learning ◽  
Multimodal Neuroimaging ◽  
Structured Sparse Learning

Robust object tracking with RGBD-based sparse learning

2017 ◽  
Vol 18 (7) ◽  
pp. 989-1001 ◽  
Author(s):  
Zi-ang Ma ◽  
Zhi-yu Xiang
Keyword(s):  
Object Tracking ◽  
Sparse Learning

scholarly journals A systematic review of structured sparse learning

2017 ◽  
Vol 18 (4) ◽  
pp. 445-463 ◽  
Author(s):  
Lin-bo Qiao ◽  
Bo-feng Zhang ◽  
Jin-shu Su ◽  
Xi-cheng Lu
Keyword(s):  
Systematic Review ◽  
Sparse Learning ◽  
Structured Sparse Learning

3D object tracking via image sets and depth-based occlusion detection

2015 ◽  
Vol 112 ◽  
pp. 146-153 ◽  
Author(s):  
Yan Chen ◽  
Yingju Shen ◽  
Xin Liu ◽  
Bineng Zhong
Keyword(s):  
Object Tracking ◽  
Occlusion Detection ◽  
3D Object

Occlusion detection and object tracking using filter banks

2011 ◽  
Author(s):  
P. Anandhakumar ◽  
J. Priyadarshini ◽  
Lakshmi Rajeswari ◽  
S. Srividhya ◽  
C. K. Niveditha
Keyword(s):  
Object Tracking ◽  
Filter Banks ◽  
Occlusion Detection

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.

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