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基于隨機投影和稀疏表示的跟蹤算法

郁道銀 王悅行 陳曉冬 汪毅

郁道銀, 王悅行, 陳曉冬, 汪毅. 基于隨機投影和稀疏表示的跟蹤算法[J]. 電子與信息學(xué)報, 2016, 38(7): 1602-1608. doi: 10.11999/JEIT151064
引用本文: 郁道銀, 王悅行, 陳曉冬, 汪毅. 基于隨機投影和稀疏表示的跟蹤算法[J]. 電子與信息學(xué)報, 2016, 38(7): 1602-1608. doi: 10.11999/JEIT151064
YU Daoyin, WANG Yuexing, CHEN Xiaodong, WANG Yi. Visual Tracking Based on Random Projection and Sparse Representation[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1602-1608. doi: 10.11999/JEIT151064
Citation: YU Daoyin, WANG Yuexing, CHEN Xiaodong, WANG Yi. Visual Tracking Based on Random Projection and Sparse Representation[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1602-1608. doi: 10.11999/JEIT151064

基于隨機投影和稀疏表示的跟蹤算法

doi: 10.11999/JEIT151064

Visual Tracking Based on Random Projection and Sparse Representation

  • 摘要: 針對目標(biāo)跟蹤過程中存在的諸多技術(shù)問題,該文提出一種魯棒的目標(biāo)跟蹤方法。首先,該文采用基于稀疏表示的全局模板描述目標(biāo)的表觀狀態(tài),通過構(gòu)造正負(fù)模板以區(qū)分目標(biāo)和背景;然后采用隨機投影法對表示模板和候選目標(biāo)進行降維,以降低算法的時間復(fù)雜度;采用粒子濾波法作為目標(biāo)的運動模型,通過多項式重采樣方法進行粒子重采樣,以保持粒子的多樣性;設(shè)計了正負(fù)模板更新策略,將正模板分為固定集和更新集,對這兩部分在相似度計算和正模板更新時采取不同的處理方法,并且在其中加入目標(biāo)遮擋的判決機制,從而可以有效避免遮擋的影響;實驗結(jié)果表明,該算法能夠準(zhǔn)確跟蹤受遮擋、運動模糊等多種復(fù)雜場景的目標(biāo),與現(xiàn)有跟蹤方法相比,所提算法具有更好的準(zhǔn)確性和穩(wěn)定性。
  • 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.
    鄧承志, 田偉, 陳盼, 等. 基于局部約束群稀疏的紅外圖像超分辨率重建[J]. 物理學(xué)報, 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.
    霍雷剛, 馮象初. 基于主成分分析和字典學(xué)習(xí)的高光譜遙感圖像去噪方法[J]. 電子與信息學(xué)報, 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.
    袁廣林, 薛模根. 基于稀疏稠密結(jié)構(gòu)表示與在線魯棒字典學(xué)習(xí)的視覺跟蹤[J]. 電子與信息學(xué)報, 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.
    齊苑辰, 吳成東, 陳東岳, 等. 基于稀疏表達的超像素跟蹤算法[J]. 電子與信息學(xué)報, 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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  • 文章訪問數(shù):  1626
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出版歷程
  • 收稿日期:  2015-09-21
  • 修回日期:  2016-04-01
  • 刊出日期:  2016-07-19

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