ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Face tracking algorithm of color and edge features based adaptive fusion

  • 1.

    School of Physics and Electronics, Shandong Normal University, Jinan 250358, China

  • 2.

    Department of Computer Science & Technology, Shandong University of Finance and Economics, Jinan 250014, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.10.005
  • Received Date: 16 May 2017
  • Rev Recd Date: 22 June 2017
  • Publish Date: 31 October 2017
    Abstract

  • Abstract

    In view of the imprecision of traditional tracking algorithms based on color histogram, a face tracking algorithm combining face multiple features based on adaptive fusion in the basic frame of particle filtering was presented.First, the color and edge features of the human face were extracted in the video sequence, while the weighted color histogram and edge orientation histogram(EOH) described facial features.Then, a self-adaptive features fusion strategy was employed to calculate particle set weight.The reliability of face tracking was enhanced by the self-adaptive features fusion strategy.Experimental results show that in the cases of complex backgrounds such as similar skin color, illumination change and so on, the proposed approach improves the tracking effect and has strong robustness.

    Abstract

    In view of the imprecision of traditional tracking algorithms based on color histogram, a face tracking algorithm combining face multiple features based on adaptive fusion in the basic frame of particle filtering was presented.First, the color and edge features of the human face were extracted in the video sequence, while the weighted color histogram and edge orientation histogram(EOH) described facial features.Then, a self-adaptive features fusion strategy was employed to calculate particle set weight.The reliability of face tracking was enhanced by the self-adaptive features fusion strategy.Experimental results show that in the cases of complex backgrounds such as similar skin color, illumination change and so on, the proposed approach improves the tracking effect and has strong robustness.
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    • References(14)
  • [1]
    王新红,王晶, 田敏, 等. 基于空间边缘方向直方图的Mean Shift跟踪算法[J].中国图象图形学报，2008, 13(3): 586-592.
    WANG Xinhong WANG Jing, TIAN Min, et al. Mean shift tracking algorithm based on spatial edge orientation histograms[J]. Journal of Image and Graphics, 2008, 13(3): 586-592.
    [2]
    WELCH G, BISHOP G. An Introduction to the Kalman Filter[M]// University of North Carolina at Chapel Hill, 2001, 8 (7): 127-132.
    [3]
    ANDERSON B D, MOORE J B. Optimal Filtering[M]. New Jersey: Prentice-Hall, 1979.
    [4]
    JULIER S J, UHLMANN J K A. New extension of the Kalman filter to non linear systems[C]// Proceedings of Aero Sense: The 11th International Symptom on Aero space/Defense Sensing, Simulation and Controls. SPIE, 1997: 182-193.
    [5]
    ISARD M, BLAKE A. Condensation-conditional density propagation for visual tracking[J]. International Journal of Computer Vision, 1998, 29(1): 5-28.
    [6]
    YANG D L，ZHANG Y L, JI R D, et al. An improved spatial histogram and particle filter face tracking[C]// Proceedings of the 8th International Conference on Genetic and Evolutionary Computing. Nanchang, China: Springer, 2014:257-267.
    [7]
    DOU J F，LI J X，ZHANG Z，et al. Face tracking with an adaptive Adaboost-based particle filter[C]// 24th Chinese Control and Decision Conference. Taiyuan, China: IEEE, 2012, 23(1): 3626-3631.
    [8]
    CHEN W M, LIN Y L, HSIEH Y H. An adaptive particle filter based method for real time face tracking[J]. Journal of Software Engineering and Applications, 2016, 6(5): 1-5.
    [9]
    田天，陈刚.基于肤色和Gabor 纹理的粒子滤波人脸跟踪[J].计算机工程，2014, 40(7): 123-127.
    TIAN Tian, CHEN Gang. Face tracking using particle filtering based on skin color and Gabor texture[J]. Computer Engineering, 2014, 40(7): 123-127.
    [10]
    SWAIN M J，BALLARD D H. Color indexing[J]. International Journal of Computer Vision, 1991, 7(1): 11-32.
    [11]
    VALISKA J，MARCHEVSKY S， KOKOSKA R. Object tracking by color-based particle filter techniques in video sequences[C]// 24th International Conference Radioelektronika. Bratislava, Slovakia: IEEE, 2014:1-4.
    [12]
    ZHU W，LEVINSON S. Edge orientation-based multi-view object recognition[J]. International Conference on Pattern Recognition，2000, 1(1): 1936-1939.
    [13]
    KAILATH T. The divergence and Bhattacharyya distance measures in signal selection[J]．IEEE Transaction on Communication Technology, 1967, 15(1): 52-60.
    [14]
    ZHOU S K，MOGHADDAM B. Appearance tracking using adaptive models in a particle filter[R]. Proceedings of Asian Conference Computer Vision, 2004.
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    [1]
    王新红,王晶, 田敏, 等. 基于空间边缘方向直方图的Mean Shift跟踪算法[J].中国图象图形学报，2008, 13(3): 586-592.
    WANG Xinhong WANG Jing, TIAN Min, et al. Mean shift tracking algorithm based on spatial edge orientation histograms[J]. Journal of Image and Graphics, 2008, 13(3): 586-592.
    [2]
    WELCH G, BISHOP G. An Introduction to the Kalman Filter[M]// University of North Carolina at Chapel Hill, 2001, 8 (7): 127-132.
    [3]
    ANDERSON B D, MOORE J B. Optimal Filtering[M]. New Jersey: Prentice-Hall, 1979.
    [4]
    JULIER S J, UHLMANN J K A. New extension of the Kalman filter to non linear systems[C]// Proceedings of Aero Sense: The 11th International Symptom on Aero space/Defense Sensing, Simulation and Controls. SPIE, 1997: 182-193.
    [5]
    ISARD M, BLAKE A. Condensation-conditional density propagation for visual tracking[J]. International Journal of Computer Vision, 1998, 29(1): 5-28.
    [6]
    YANG D L，ZHANG Y L, JI R D, et al. An improved spatial histogram and particle filter face tracking[C]// Proceedings of the 8th International Conference on Genetic and Evolutionary Computing. Nanchang, China: Springer, 2014:257-267.
    [7]
    DOU J F，LI J X，ZHANG Z，et al. Face tracking with an adaptive Adaboost-based particle filter[C]// 24th Chinese Control and Decision Conference. Taiyuan, China: IEEE, 2012, 23(1): 3626-3631.
    [8]
    CHEN W M, LIN Y L, HSIEH Y H. An adaptive particle filter based method for real time face tracking[J]. Journal of Software Engineering and Applications, 2016, 6(5): 1-5.
    [9]
    田天，陈刚.基于肤色和Gabor 纹理的粒子滤波人脸跟踪[J].计算机工程，2014, 40(7): 123-127.
    TIAN Tian, CHEN Gang. Face tracking using particle filtering based on skin color and Gabor texture[J]. Computer Engineering, 2014, 40(7): 123-127.
    [10]
    SWAIN M J，BALLARD D H. Color indexing[J]. International Journal of Computer Vision, 1991, 7(1): 11-32.
    [11]
    VALISKA J，MARCHEVSKY S， KOKOSKA R. Object tracking by color-based particle filter techniques in video sequences[C]// 24th International Conference Radioelektronika. Bratislava, Slovakia: IEEE, 2014:1-4.
    [12]
    ZHU W，LEVINSON S. Edge orientation-based multi-view object recognition[J]. International Conference on Pattern Recognition，2000, 1(1): 1936-1939.
    [13]
    KAILATH T. The divergence and Bhattacharyya distance measures in signal selection[J]．IEEE Transaction on Communication Technology, 1967, 15(1): 52-60.
    [14]
    ZHOU S K，MOGHADDAM B. Appearance tracking using adaptive models in a particle filter[R]. Proceedings of Asian Conference Computer Vision, 2004.
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