基于随机投影和稀疏表示的跟踪算法

郁道银; 王悦行; 陈晓冬; 汪毅

doi:10.11999/JEIT151064

基于随机投影和稀疏表示的跟踪算法

doi: 10.11999/JEIT151064

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出版历程
- 收稿日期: 2015-09-21
- 修回日期: 2016-04-01
- 刊出日期: 2016-07-19

Visual Tracking Based on Random Projection and Sparse Representation

摘要

摘要: 针对目标跟踪过程中存在的诸多技术问题，该文提出一种鲁棒的目标跟踪方法。首先，该文采用基于稀疏表示的全局模板描述目标的表观状态，通过构造正负模板以区分目标和背景；然后采用随机投影法对表示模板和候选目标进行降维，以降低算法的时间复杂度；采用粒子滤波法作为目标的运动模型，通过多项式重采样方法进行粒子重采样，以保持粒子的多样性；设计了正负模板更新策略，将正模板分为固定集和更新集，对这两部分在相似度计算和正模板更新时采取不同的处理方法，并且在其中加入目标遮挡的判决机制，从而可以有效避免遮挡的影响；实验结果表明，该算法能够准确跟踪受遮挡、运动模糊等多种复杂场景的目标，与现有跟踪方法相比，所提算法具有更好的准确性和稳定性。
- 目标跟踪 /
- 稀疏表示 /
- 随机投影 /
- 模板更新 /
- 重采样策略
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.
- Object tracking /
- Sparse representation /
- Random Projection (RP) /
- Template updating /
- Resample method

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参考文献(26)

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.

DONOHO D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

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.

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.

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.

XUE M and LING H. Robust visual tracking using L1 minimization[C]. IEEE International Conference on Computer Vision, Kyoto, Japan, 2009: 1436-1443.

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.

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.

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.

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.

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.

TURK M and PENTLAND A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86.

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.

ACHLIOPTAS D. Database-friendly random projections: Johnson-Lindenstrauss with binary coins[J]. Journal of Computer and System Sciences, 2003, 66(4): 671-687.

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.

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.

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.

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.

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.

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.

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.

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