ISSN 0253-2778

CN 34-1054/N

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Particle filter tracking based on feature-learning and feature-memory template update mechanism

  • 1.

    Department of Automation, University of Science and Technology of China, Hefei 230027, China

  • 2.

    Institute of Advanced Manufacturing Technology, Hefei Institute of Physical Science, Chinese Academy of Science, Hefei 230031, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.04.006
  • Received Date: 09 August 2013
  • Accepted Date: 11 January 2014
  • Rev Recd Date: 11 January 2014
  • Publish Date: 30 April 2014
    Abstract

  • Abstract

    The diversity of object motion and the complexity of background decrease the robustness of object tracking. Similarity of background colors, changes in illumination and object deformation lower the accuracy of the object template and the robustness of object tracking. To deal with this problem, a template update mechanism based on feature-learning and feature-memory was proposed. The algorithm built an object template library by preserving abundant information of the object. By matching the object with the object template library, the state of the object was obtained and the object was then tracked by particle filter. Experimental results show that the proposed method has better accuracy and robustness than the particle filter based on traditional object template update strategies.

    Abstract

    The diversity of object motion and the complexity of background decrease the robustness of object tracking. Similarity of background colors, changes in illumination and object deformation lower the accuracy of the object template and the robustness of object tracking. To deal with this problem, a template update mechanism based on feature-learning and feature-memory was proposed. The algorithm built an object template library by preserving abundant information of the object. By matching the object with the object template library, the state of the object was obtained and the object was then tracked by particle filter. Experimental results show that the proposed method has better accuracy and robustness than the particle filter based on traditional object template update strategies.
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    • References(37)
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    Wang Q, Chen F, Xu W L, et al. Online discriminative object tracking with local sparse representation[C]// IEEE Workshop on Applications of Computer Vision. Breckenridge, USA: IEEE Computer Society, 2012: 425-432.
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    Zhong W, Lu H C, Yang M H. Robust object tracking via sparsity-based collaborative model[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE Computer Society, 2012: 1 838-1 845.
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    Frintrop S, Kessel M. Most salient region tracking[C]// IEEE International Conference on Robotics and Automation. Kobe, Japan: IEEE Computer Society, 2009: 1 869-1 874.
    [28]
    Xu X X, Wang Z L, Chen Z H. Visual tracking model based on feature-imagination and its application[C]// International Conference on Multimedia Information Networking and Security. Nanjing, China: IEEE Computer Society, 2010: 370-374.
    [29]
    Kong A, Liu J S, Wong W H. Sequential imputations and Bayesian missing data problems[J]. Journal of the American Statistical Association, 1994, 89(425): 278-288.
    [30]
    Liu J S. Metropolized independent sampling with comparisons to rejection sampling and importance sampling[J]. Statistics and Computing, 1996, 6(2): 113-119.
    [31]
    Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering[J]. Statistics and Computing, 2000, 10(3): 197-208.
    [32]
    Posner M I, Petersen S E. The attention system of the human brain[J]. Annual Review of Neuroscience, 1990, 13(1): 25-42.
    [33]
    Liu H, Shi Y. Robust visual tracking based on selective attention shift[C]// IEEE International Conference on Control Applications. Saint Peterburg, Russian: IEEE Press, 2009: 1 176-1 179.
    [34]
    Zhang G, Yuan Z J, Zheng N N, et al. Visual saliency based object tracking[C]// Proceedings of the 9th Asian Conference on Computer Vision. Xi’an, China: Springer, 2009: 193-203.
    [35]
    Yang G, Liu H. Visual attention & multi-cue fusion based human motion tracking method[C]// 6th International Conference on Natural Computation. Yantai, China: IEEE Circults And Systems Society, 2010: 2 044-2 054.
    [36]
    Espinace P, Soto A. Improving the selection and detection of visual landmarks through object tracking[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Anchroage, AK: IEEE Computer Society, 2008: 1-7.
    [37]
    朱明清，王智灵，陈宗海. 基于改进Bhattacharyya系数的粒子滤波视觉跟踪算法[J].控制与决策, 2012, 27(10): 1-5.
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    [1]
    Eom K Y, Ahn T K, Kim G J, et al. Fast object tracking in intelligent surveillance system[C]// International Conference on Computational Science and Its Applications. Seoul, Korea: Springer, 2009: 749-763.
    [2]
    Kettnaker V, Zabih R. Bayesian multi camera surveillance[C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, USA: IEEE Computer Society, 1999: 253-259.
    [3]
    Shan C F, Tan T N, Wei Y C. Real-time hand tracking using a mean shift embedded particle filter[J]. Pattern Recognition, 2007, 40(7): 1 958-1 970.
    [4]
    Zhao T, Aggarwal M, Kumar R, et al. Real-time wide area multi-camera stereo tracking[C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE Computer Society, 2005: 976-983.
    [5]
    Siagian C, Itti L. Biologically inspired mobile robot vision localization[J]. IEEE Transactions on Robotics, 2009, 25(4): 861-873.
    [6]
    Yilmaz A, Javed O, Shah M. Object tracking: A survey[J]. ACM Computing Surveys, 2006, 38(4): 1-45.
    [7]
    Broida T J, Chellappa R. Estimation of object motion parameters from noisy images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(1): 90-99.
    [8]
    Isard M, Blake A. Condensation-conditional density propagation for visual tracking[J]. International Journal on Computer Vision, 1998, 29(1): 5-28.
    [9]
    Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577.
    [10]
    Allen J G, Xu R Y D, Jin J S. Object tracking using CamShift algorithm and multiple quantized feature spaces[C]// Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing. Darlinghurst, Australia: Australia Computer Society, 2004: 3-7.
    [11]
    Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, USA: IEEE Computer Society, 2000, 2: 142-149.
    [12]
    Liu T L, Chen H T. Real-time tracking using trust-region methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(3):397-401.
    [13]
    Hager G D, Belhumeur P N. Efficient region tracking with parametric models of geometry and illumination[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(10): 1 025-1 039.
    [14]
    Pérez P, Hue C, Vermaak J, et al. Color-based probabilistic tracking[C]// Proceedings of the 7th European Conference on Computer Vision. Copenhagen, Denmark: Springer, 2002: 661-675.
    [15]
    Reynolds J. Autonomous underwater vehicle: Vision system[D]. Canberra, Australia, Department of Engineering, Australian National University, 1998.
    [16]
    Ross D A, Lim J, Lin R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1-3): 125-141.
    [17]
    Collins R T, Liu Y X. On-line selection of discriminative tracking features[C]// Proceedings of the 9th IEEE Conference on International Conference on Computer Vision. Nice, France: IEEE Computer Society, 2003: 346-352.
    [18]
    Nummiaro K, Koller-Meier E, Van Gool L. An adaptive color-based particle filter[J]. Image and Vision Computing, 2003, 21(1): 99-110.
    [19]
    Ross D, Lim J, Yang M H. Adaptive probabilistic visual tracking with incremental subspace update[C]// Proceedings of the 8th European Conference on Computer Vision. Prague, Czech Republic: Springer, 2004: 470-482.
    [20]
    Han B, Davis L. On-line density-based appearance modeling for object tracking[C]// Proceedings of the 10th IEEE Conference on International Conference on Computer Vision. Beijing, China: IEEE Computer Society, 2005, 2: 1 492-1 499.
    [21]
    Zhou Q H, Lu H C, Yang M H. Online multiple support instance tracking[C]// IEEE International Conference on Automatic Face and Gesture Recognition. Santa Barbara, USA: IEEE Computer Society, 2011: 545-552.
    [22]
    Kwon J, Lee K M. Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping Monte Carlo sampling[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE Computer Society, 2009: 1 208-1 215.
    [23]
    Sun X, Yao H X, Zhang S P, et al. On-line discriminative appearance modeling for robust object tracking[C]// International Conference on Pervasive Computing Signal Processing and Applications. Harbin, China: IEEE Press, 2010: 78-81.
    [24]
    Wang Q, Chen F, Xu W L, et al. Online discriminative object tracking with local sparse representation[C]// IEEE Workshop on Applications of Computer Vision. Breckenridge, USA: IEEE Computer Society, 2012: 425-432.
    [25]
    Jia X, Lu H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE Computer Society, 2012: 1 822-1 829.
    [26]
    Zhong W, Lu H C, Yang M H. Robust object tracking via sparsity-based collaborative model[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE Computer Society, 2012: 1 838-1 845.
    [27]
    Frintrop S, Kessel M. Most salient region tracking[C]// IEEE International Conference on Robotics and Automation. Kobe, Japan: IEEE Computer Society, 2009: 1 869-1 874.
    [28]
    Xu X X, Wang Z L, Chen Z H. Visual tracking model based on feature-imagination and its application[C]// International Conference on Multimedia Information Networking and Security. Nanjing, China: IEEE Computer Society, 2010: 370-374.
    [29]
    Kong A, Liu J S, Wong W H. Sequential imputations and Bayesian missing data problems[J]. Journal of the American Statistical Association, 1994, 89(425): 278-288.
    [30]
    Liu J S. Metropolized independent sampling with comparisons to rejection sampling and importance sampling[J]. Statistics and Computing, 1996, 6(2): 113-119.
    [31]
    Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering[J]. Statistics and Computing, 2000, 10(3): 197-208.
    [32]
    Posner M I, Petersen S E. The attention system of the human brain[J]. Annual Review of Neuroscience, 1990, 13(1): 25-42.
    [33]
    Liu H, Shi Y. Robust visual tracking based on selective attention shift[C]// IEEE International Conference on Control Applications. Saint Peterburg, Russian: IEEE Press, 2009: 1 176-1 179.
    [34]
    Zhang G, Yuan Z J, Zheng N N, et al. Visual saliency based object tracking[C]// Proceedings of the 9th Asian Conference on Computer Vision. Xi’an, China: Springer, 2009: 193-203.
    [35]
    Yang G, Liu H. Visual attention & multi-cue fusion based human motion tracking method[C]// 6th International Conference on Natural Computation. Yantai, China: IEEE Circults And Systems Society, 2010: 2 044-2 054.
    [36]
    Espinace P, Soto A. Improving the selection and detection of visual landmarks through object tracking[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Anchroage, AK: IEEE Computer Society, 2008: 1-7.
    [37]
    朱明清，王智灵，陈宗海. 基于改进Bhattacharyya系数的粒子滤波视觉跟踪算法[J].控制与决策, 2012, 27(10): 1-5.
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