|
|
|
| Visual Tracking Based on Random Projection and Sparse Representation |
| YU Daoyin WANG Yuexing CHEN Xiaodong WANG Yi |
(College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China)
(Key Laboratory of Opto-Electronics Information Technology of Ministry of Education,Tianjin University, Tianjin 300072, China) |
|
|
|
|
Abstract A robust object tracking method is proposed to deal with technical issues during tracking. Firstly, the global template based on sparse representation is used to describe object appearance, while positive and negative modules are built to separate the object from the background. Then, Random Projection (RP) is used to reduce the dimension of modules and candidate objects, which could release the calculation burden. Furthermore, the Particle Filter (PF) is used as the object motion model, and the multi-normal resample method is used to maintain the diversity of particles. To alleviate module drift problem, the positive module is divided into static module and changeable module, while different modules are dealt with different ways, and sparse reconstruction error is used to determine whether the object is occluded. Experiment results on numerous challenging videos show that the proposed method has better performance in accuracy and stability in comparison with state-of-the-art tracking methods.
|
|
Received: 21 September 2015
Published: 24 May 2016
|
|
|
|
Corresponding Authors:
CHEN Xiaodong
E-mail: xdchen@tju.edu.cn
|
|
|
|
| [1] |
ZHUANG B H, LU H C, XIAO Z Y, et al. Visual tracking via discriminative sparse similarity map[J]. IEEE Transactions on Image Processing, 2014, 23(4): 1872-1881.
|
| [2] |
DONOHO D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
|
| [3] |
MA C F, JUNG Juneyoung, KIM Seungwook, et al. Random projection-based partial feature extraction for robust face recognition[J]. Neurocomputing, 2015, 149C: 1232-1244. doi: 10.1016/J.neucom.2014.09.004.
|
| [4] |
邓承志, 田伟, 陈盼, 等. 基于局部约束群稀疏的红外图像超分辨率重建[J]. 物理学报, 2014, 63(4): 044202-044208. doi: 10.7498/aps.63.044202.
|
|
DENG Chengzhi, TIAN Wei, CHEN Pan, et al. Infrared image super-resolution via locality-constrained group sparse model[J]. Acta Physica Sinica, 2014, 63(4): 044202-044208. doi: 10.7498/aps.63.044202.
|
| [5] |
霍雷刚, 冯象初. 基于主成分分析和字典学习的高光谱遥感图像去噪方法[J]. 电子与信息学报, 2014, 36(11): 2723-2729. doi: 10.3724/SP.J.1146.2013.01840.
|
|
HUO Leigang and FENG Xiangchu. Denoising of hyperspectral remote sensing image based on principal component analysis and dictionary learning[J]. Journal of Electronics & Information Technology, 2014, 36(11): 2723-2729. doi: 10.3724/SP.J.1146.2013.01840.
|
| [6] |
XUE M and LING H. Robust visual tracking using L1 minimization[C]. IEEE International Conference on Computer Vision, Kyoto, Japan, 2009: 1436-1443.
|
| [7] |
BAO C L, WU Y, LING H B, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]. Proceedings of the International Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 1830-1837.
|
| [8] |
ZHONG W, LU H C, and YANG M-H. Robust object tracking via sparsity-based collaborative model[C]. Proceedings of the International Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 1838-1845.
|
| [9] |
袁广林, 薛模根. 基于稀疏稠密结构表示与在线鲁棒字典学习的视觉跟踪[J]. 电子与信息学报, 2015, 37(3): 536-542. doi: 10.11999/JEIT140507.
|
|
YUAN Guanglin and XUE Mogen. Visual tracking based on sparse dense structure representation and online robust
|
|
dictionary learning[J]. Journal of Electronics & Information Technology, 2015, 37(3): 536-542. doi: 10.11999/JEIT140507.
|
| [10] |
齐苑辰, 吴成东, 陈东岳, 等. 基于稀疏表达的超像素跟踪算法[J]. 电子与信息学报, 2015, 37(3): 529-535. doi: 10.11999 /JEIT140374.
|
|
QI Yuanchen, WU Chengdong, CHEN Dongyue, et al. Superpixel tracking based on sparse representation[J]. Journal of Electronics & Information Technology, 2015, 37(3): 529-535. doi: 10.11999/JEIT140374.
|
| [11] |
ROSS David A, LIM Jongwoo, LIN Rueisung, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1/3): 125-141.
|
| [12] |
TURK M and PENTLAND A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86.
|
| [13] |
WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
|
| [14] |
ACHLIOPTAS D. Database-friendly random projections: Johnson-Lindenstrauss with binary coins[J]. Journal of Computer and System Sciences, 2003, 66(4): 671-687.
|
| [15] |
LI T, SATTAR T P, and SUN S. Deterministic resampling: unbiased sampling to avoid sample impoverishment in particle filters[J]. Signal Processing, 2012, 92(7): 1637-1645.
|
| [16] |
GORDON N J, SALMOND D J, and SMITH A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation[J]. IEE Proceedings F-Radar and Signal Processing, 1993, 140(2): 107-113.
|
| [17] |
KALAL Z, MATAS J, and MIKOLAJCZYK K. P-N learning: bootstrapping binary classifiers by structural constraints[C]. Proceedings of the International Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010: 49-56.
|
| [18] |
BABENKO B, YANG M H, and BELONGIE S. Visual tracking with online multiple instance learning[C]. Proceedings of the International Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 983-990.
|
| [19] |
ADAM A, RIVLIN E, and SHIMSHONI I. Robust fragments- based tracking using the integral histogram[C]. Proceedings of the International Conference on Computer Vision and Pattern Recognition, New York, USA, 2006: 798-805.
|
| [20] |
KWON J and LEE K M. Visual tracking decomposition[C]. Proceedings of the International Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010: 1269-1276.
|
| [21] |
WANG D, LU H, and YANG M H. Online object tracking with sparse prototypes[J]. IEEE Transactions on Image Processing, 2013, 22(1): 314-325.
|
|
|
|
