Robust object tracking with RGBD-based sparse learning

Zi-ang MA; Zhi-yu XIANG

doi:10.1631/FITEE.1601338

Your Location：

Home >

Browse articles >

Robust object tracking with RGBD-based sparse learning

Regular Papers | Updated：2022-05-19

- Robust object tracking with RGBD-based sparse learning
- 基于RGBD和稀疏学习的鲁棒目标跟踪
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 7, Pages: 989-1001(2017)
- Affiliations：
  
  College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
  Zhejiang Provincial Key Laboratory of Information Network Technology, Hangzhou 310027, China
- Author bio：
  
  Zhi-yu XIANG, E-mail: xiangzy@zju.edu.cn
- Funds：
- DOI：10.1631/FITEE.1601338
  CLC： TP391
- Received：14 June 2016，
  
  Revised：2017-;7-13，
  
  Published：2017-07
- Accepted：
Scan QR Code
Zi-ang MA, Zhi-yu XIANG. Robust object tracking with RGBD-based sparse learning[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(7): 989-1001.
DOI：

Zi-ang MA, Zhi-yu XIANG. Robust object tracking with RGBD-based sparse learning[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(7): 989-1001. DOI： 10.1631/FITEE.1601338.

摘要

鲁棒目标跟踪近年来成为计算机视觉领域一项重要的且极具挑战性的研究方向。随着深度传感器的普及，深度信息因其对光照变化与遮挡表现出一定的鲁棒性而被广泛应用于视觉目标跟踪算法中。本文提出了一种基于RGBD和稀疏学习的跟踪算法，从三个方面将深度信息应用到稀疏学习跟踪框架。首先将深度图像特征结合现有的基于彩色图像的视觉特征用于目标外观的鲁棒特征描述。为了适应跟踪过程中的各种遮挡情况，我们设计了一种特殊的遮挡物模板用于增广现有的超完备字典。最后，我们进一步提出了一种基于深度信息的遮挡物检测方法用于有效地指示模板更新。基于KITTI和Princeton数据集的大量实验证明了所提出算法的跟踪效果优于时下最先进的多种跟踪器，包括基于稀疏学习的跟踪以及基于RGBD的跟踪。

Abstract

Robust object tracking has been an important and challenging research area in the field of computer vision for decades. With the increasing popularity of affordable depth sensors

range data is widely used in visual tracking for its ability to provide robustness to varying illumination and occlusions. In this paper

a novel RGBD and sparse learning based tracker is proposed. The range data is integrated into the sparse learning framework in three respects. First

an extra depth view is added to the color image based visual features as an independent view for robust appearance modeling. Then

a special occlusion template set is designed to replenish the existing dictionary for handling various occlusion conditions. Finally

a depth-based occlusion detection method is proposed to efficiently determine an accurate time for the template update. Extensive experiments on both KITTI and Princeton data sets demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms

including both sparse learning and RGBD based methods.

关键词

Keywords

references

S. Avidan . . Ensemble tracking . . IEEE Trans. Patt. Anal. Mach. Intell. , , 2007 . . 29 ( ( 2 ): ): 261 - - 271 . . DOI: 10.1109/TPAMI.2007.35 http://doi.org/10.1109/TPAMI.2007.35 . .

B. Babenko , , , M.H. Yang , , , S. Belongie . . Visual tracking with online multiple instance learning . . IEEE Conf. on Computer Vision and Pattern Recognition , , 2009 . . p.983 - - 990 . . DOI: 10.1109/CVPR.2009.5206737 http://doi.org/10.1109/CVPR.2009.5206737 . .

C.L. Bao , , , Y. Wu , , , H.B. Ling , , , 等 . . Real time robust L1 tracker using accelerated proximal gradient approach . . IEEE Conf. on Computer Vision and Pattern Recognition , , 2012 . . p.1830 - - 1837 . . DOI: 10.1109/CVPR.2012.6247881 http://doi.org/10.1109/CVPR.2012.6247881 . .

M.J. Black , , , A.D. Jepson . . EigenTracking: robust matching and tracking of articulated objects using a view-based representation . . Int. J. Comput. Vis. , , 1998 . . 26 ( ( 1 ): ): 63 - - 84 . . DOI: 10.1023/A:1007939232436 http://doi.org/10.1023/A:1007939232436 . .

E.J. Candes , , , J.K. Romberg , , , T. Tao . . Stable signal recovery from incomplete and inaccurate measurements . . Commun. Pure Appl. Math. , , 2006 . . 59 ( ( 8 ): ): 1207 - - 1223 . . DOI: 10.1002/cpa.20124 http://doi.org/10.1002/cpa.20124 . .

X. Chen , , , W.K. Pan , , , J.T. Kwok , , , 等 . . Accelerated gradient method for multi-task sparse learning problem . . 9th IEEE Int. Conf. on Data Mining , , 2009 . . p.746 - - 751 . . DOI: 10.1109/ICDM.2009.128 http://doi.org/10.1109/ICDM.2009.128 . .

W. Choi , , , C. Pantofaru , , , S. Savarese . . Detecting and tracking people using an RGB-D camera via multiple detector fusion . . IEEE Int. Conf. on Computer Vision Workshops , , 2011 . . p.1076 - - 1083 . . DOI: 10.1109/ICCVW.2011.6130370 http://doi.org/10.1109/ICCVW.2011.6130370 . .

D. Comaniciu , , , V. Ramesh , , , P. Meer . . Kernel-based object tracking . . IEEE Trans. Patt. Anal. Mach. Intell. , , 2003 . . 25 ( ( 5 ): ): 564 - - 577 . . DOI: 10.1109/TPAMI.2003.1195991 http://doi.org/10.1109/TPAMI.2003.1195991 . .

N. Dalal , , , B. Triggs . . Histograms of oriented gradients for human detection . . IEEE Computer Society Conf. on Computer Vision and Pattern Recognition , , 2005 . . p.886 - - 893 . . DOI: 10.1109/CVPR.2005.177 http://doi.org/10.1109/CVPR.2005.177 . .

D.L. Donoho . . Compressed sensing . . IEEE Trans. Inform. Theory , , 2006 . . 52 ( ( 4 ): ): 1289 - - 1306 . . DOI: 10.1109/TIT.2006.871582 http://doi.org/10.1109/TIT.2006.871582 . .

Z.B. Hong , , , X. Mei , , , D. Prokhorov , , , 等 . . Tracking via robust multi-task multi-view joint sparse representation . . IEEE Int. Conf. on Computer Vision , , 2013 . . p.649 - - 656 . . DOI: 10.1109/ICCV.2013.86 http://doi.org/10.1109/ICCV.2013.86 . .

X.Y. Lan , , , A. Ma , , , P. Yuen . . Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation . . IEEE Int. Conf. on Computer Vision and Pattern Recognition , , 2014 . . p.1194 - - 1201 . . DOI: 10.1109/CVPR.2014.156 http://doi.org/10.1109/CVPR.2014.156 . .

H.B. Ling , , , L. Bai , , , E. Blasch , , , 等 . . Robust infrared vehicle tracking across target pose change using L1 regularization . . IEEE Conf. on Information Fusion , , 2010 . . p.1 - - 8 . . DOI: 10.1109/ICIF.2010.5711902 http://doi.org/10.1109/ICIF.2010.5711902 . .

B.Y. Liu , , , L. Yang , , , J.Z. Huang , , , 等 . . Robust and fast collaborative tracking with two stage sparse optimization . . European Conf. on Computer Vision , , 2010 . . p.624 - - 637 . . DOI: 10.1007/978-3-642-15561-1_45 http://doi.org/10.1007/978-3-642-15561-1_45 . .

M. Luber , , , L. Spinello , , , K.O. Arras . . People tracking in RGB-D data with on-line boosted target models . . IEEE/RSJ Int. Conf. on Intelligent Robots and Systems , , 2011 . . p.3844 - - 3849 . . DOI: 10.1109/IROS.2011.6095075 http://doi.org/10.1109/IROS.2011.6095075 . .

Z.A. Ma , , , Z.Y. Xiang . . Robust visual tracking via binocular multi-task multi-view joint sparse representation . . SAI Intelligent Systems Conf. , , 2015 . . p.714 - - 722 . . DOI: 10.1109/IntelliSys.2015.7361219 http://doi.org/10.1109/IntelliSys.2015.7361219 . .

X. Mei , , , H.B. Ling . . Robust visual tracking using ℓ 1 minimization . . IEEE 12th Int. Conf. on Computer Vision , , 2009 . . p.1436 - - 1443 . . DOI: 10.1109/ICCV.2009.5459292 http://doi.org/10.1109/ICCV.2009.5459292 . .

X. Mei , , , H.B. Ling . . Robust visual tracking and vehicle classification via sparse representation . . IEEE Trans. Patt. Anal. Mach. Intell. , , 2011 . . 33 ( ( 11 ): ): 2259 - - 2272 . . DOI: 10.1109/TPAMI.2011.66 http://doi.org/10.1109/TPAMI.2011.66 . .

X. Mei , , , H.B. Ling , , , Y. Wu , , , 等 . . Minimum error bounded efficient ℓ 1 tracker with occlusion detection . . IEEE Conf. on Computer Vision and Pattern Recognition , , 2011 . . p.1257 - - 1264 . . DOI: 10.1109/CVPR.2011.5995421 http://doi.org/10.1109/CVPR.2011.5995421 . .

T. Ojala , , , M. Pietikäinen , , , T. Mäenpää . . Multiresolution gray-scale and rotation invariant texture classification with local binary patterns . . IEEE Trans. Patt. Anal. Mach. Intell. , , 2002 . . 24 ( ( 7 ): ): 971 - - 987 . . DOI: 10.1109/TPAMI.2002.1017623 http://doi.org/10.1109/TPAMI.2002.1017623 . .

S.C. Pei , , , C.N. Lin . . Image normalization for pattern recognition . . Image Vis. Comput. , , 1995 . . 13 ( ( 10 ): ): 711 - - 723 . . DOI: 10.1016/0262-8856(95)98753-G http://doi.org/10.1016/0262-8856(95)98753-G . .

F. Porikli , , , O. Tuzel , , , P. Meer . . Covariance tracking using model update based on Lie algebra . . IEEE Computer Society Conf. on Computer Vision and Pattern Recognition , , 2006 . . p.728 - - 735 . . DOI: 10.1109/CVPR.2006.94 http://doi.org/10.1109/CVPR.2006.94 . .

D.A. Ross , , , J. Lim , , , R.S. Lin , , , 等 . . Incremental learning for robust visual tracking . . Int. J. Comput. Vis. , , 2008 . . 77 ( ( 1-3 ): ): 125 - - 141 . . DOI: 10.1007/s11263-007-0075-7 http://doi.org/10.1007/s11263-007-0075-7 . .

S.R. Song , , , J.X. Xiao . . Tracking revisited using RGBD camera: unified benchmark and baselines . . IEEE Int. Conf. on Computer Vision , , 2013 . . p.233 - - 240 . . DOI: 10.1109/ICCV.2013.36 http://doi.org/10.1109/ICCV.2013.36 . .

O. Williams , , , A. Blake , , , R. Cipolla . . Sparse Bayesian learning for efficient visual tracking . . IEEE Trans. Patt. Anal. Mach. Intell. , , 2005 . . 27 ( ( 8 ): ): 1292 - - 1304 . . DOI: 10.1109/TPAMI.2005.167 http://doi.org/10.1109/TPAMI.2005.167 . .

J. Wright , , , A.Y. Yang , , , A. Ganesh , , , 等 . . Robust face recognition via sparse representation . . IEEE Trans. Patt. Anal. Mach. Intell. , , 2009 . . 31 ( ( 2 ): ): 210 - - 227 . . DOI: 10.1109/TPAMI.2008.79 http://doi.org/10.1109/TPAMI.2008.79 . .

Y. Wu , , , J. Lim , , , M.H. Yang . . Online object tracking: a benchmark . . IEEE Conf. on Computer Vision and Pattern Recognition , , 2013 . . p.2411 - - 2418 . . DOI: 10.1109/CVPR.2013.312 http://doi.org/10.1109/CVPR.2013.312 . .

M. Yang , , , L. Zhang . . Gabor feature-based sparse representation for face recognition with Gabor occlusion dictionary . . European Conf. on Computer Vision , , 2010 . . p.448 - - 461 . . DOI: 10.1007/978-3-642-15567-3_33 http://doi.org/10.1007/978-3-642-15567-3_33 . .

A. Yilmaz , , , O. Javed , , , M. Shah . . Object tracking: a survey . . ACM Comput. Surv. , , 2006 . . 38 ( ( 4 ): ): 43 - - 56 . . DOI: 10.1145/1177352.1177355 http://doi.org/10.1145/1177352.1177355 . .

Z.Z. Yin , , , R.T. Collins . . Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking . . IEEE Conf. on Computer Vision and Pattern Recognition , , 2008 . . p.1 - - 8 . . DOI: 10.1109/CVPR.2008.4587542 http://doi.org/10.1109/CVPR.2008.4587542 . .

K. Zhang , , , L. Zhang , , , M.H. Yang . . Real-time compressive tracking . . European Conf. on Computer Vision , , 2012 . . p.864 - - 877 . . DOI: 10.1007/978-3-642-33712-3_62 http://doi.org/10.1007/978-3-642-33712-3_62 . .

T.Z. Zhang , , , B. Ghanem , , , S. Liu , , , 等 . . Low-rank sparse learning for robust visual tracking . . European Conf. on Computer Vision , , 2012a . . p.470 - - 484 . . DOI: 10.1007/978-3-642-33783-3_34 http://doi.org/10.1007/978-3-642-33783-3_34 . .

T.Z. Zhang , , , B. Ghanem , , , S. Liu , , , 等 . . Robust visual tracking via multi-task sparse learning . . IEEE Conf. on Computer Vision and Pattern Recognition , , 2012b . . p.2042 - - 2049 . . DOI: 10.1109/CVPR.2012.6247908 http://doi.org/10.1109/CVPR.2012.6247908 . .

T.Z. Zhang , , , B. Ghanem , , , S. Liu , , , 等 . . Robust visual tracking via structured multi-task sparse learning . . Int. J. Comput. Vis. , , 2013 . . 101 ( ( 2 ): ): 367 - - 383 . . DOI: 10.1007/s11263-012-0582-z http://doi.org/10.1007/s11263-012-0582-z . .

T.Z. Zhang , , , S. Liu , , , N. Ahuja , , , 等 . . Robust visual tracking via consistent low-rank sparse learning . . Int. J. Comput. Vis. , , 2015a . . 111 ( ( 2 ): ): 171 - - 190 . . DOI: 10.1007/s11263-014-0738-0 http://doi.org/10.1007/s11263-014-0738-0 . .

T.Z. Zhang , , , S. Liu , , , C.S. Xu , , , 等 . . Structural sparse tracking . . IEEE Conf. on Computer Vision and Pattern Recognition , , 2015b . . p.150 - - 158 . . DOI: 10.1109/CVPR.2015.7298610 http://doi.org/10.1109/CVPR.2015.7298610 . .

Z.Y. Zhang . . Iterative point matching for registration of free-form curves and surfaces . . Int. J. Comput. Vis. , , 1994 . . 13 ( ( 2 ): ): 119 - - 152 . . DOI: 10.1007/BF01427149 http://doi.org/10.1007/BF01427149 . .

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Building trust networks in the absence of trust relations

A robust object tracking framework based on a reliable point assignment algorithm#

Related Author

Xin WANG

Ying WANG

Jian-hua GUO

Rong-feng ZHANG

Ting DENG

Gui-hong WANG

Jing-lun SHI

Quan-sheng GUAN

Related Institution

College of Computer Science and Technology, Jilin University

School of Computer Technology and Engineering, Changchun Institute of Technology

School of Mathematics and Statistics, Northeast Normal University

Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education

Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰