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SUN Shou-qun, LIU Kang-ya, LIU Shuo-yan, LU: Xiao-jun, ZHAN Xuan. Moving target detection in complex environment of railway station[J]. Journal of Traffic and Transportation Engineering, 2013, 13(3): 113-120. doi: 10.19818/j.cnki.1671-1637.2013.03.016
Citation: SUN Shou-qun, LIU Kang-ya, LIU Shuo-yan, LU: Xiao-jun, ZHAN Xuan. Moving target detection in complex environment of railway station[J]. Journal of Traffic and Transportation Engineering, 2013, 13(3): 113-120. doi: 10.19818/j.cnki.1671-1637.2013.03.016
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Moving target detection in complex environment of railway station

doi: 10.19818/j.cnki.1671-1637.2013.03.016
  • 1.

    School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

  • 2.

    Institute of Computing Technology, China Academy of Railway Sciences, Beijing 100081, China

More Information
  • Author Bio:

    SUN Shourqun(1964-), male, associate professor, PhD, +86-21-55275056, jrssq@163.com

  • Received Date: 2013-01-18
  • Publish Date: 2013-06-25
    Abstract
  • Traditional GMM(Gaussian mixture model) was dived into background layer, completion layer and noise layer by using hierarchical organization.Diverse update mechanisms were applied in different layers. In order to correct possible misjudgment, promotion and downgraded mechanisms were introduced between layers. To eliminate noise, noise layer was updated by using noise filter based on contour detection. In order to improve the adaptability for changing background, pseudo foreground area was detected by using histogram matching. The detection effect of improved GMM was verified by using the videos of station and parking lot. Verification result indicates that the problem of long-term static target being merged into background is settled. The impact of light mutations or camera noise is reduced. The updating speed of model increases when the background changes. Detection speed increases by 10% compared with traditional GMM.The efficiency and accuracy of moving target detection in railway station are improved by improved GMM, and the foundation for intelligent video analysis is laid.

     

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    • References(18)
  • [1]
    ZHENG Jin, LI Bo. Prospects and current studies on motion object detection in video sequences[J]. Application Research of Computers, 2008, 25(12): 3534-3540. (in Chinese). doi: 10.3969/j.issn.1001-3695.2008.12.004
    [2]
    STAUFFER C, GRIMSON W E L. Learning patterns of activity using real-time tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757. doi: 10.1109/34.868677
    [3]
    YANG Guo-liang, WANG Zhi-liang, MU Shi-tang, et al. An improved optical flow algorithm[J]. Computer Engineering, 2006, 32(15): 187-188, 226. (in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-JSJC200615065.htm
    [4]
    LOU Lu, ZHAO Ling, GENG Tao. Detecting and tracking method of movingvehicle[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 107-133. (in Chinese). http://transport.gfscw.cn/article/id/201204014
    [5]
    ZIVKOVIC Z, HEIJDEN F. Efficient adaptive density estimation per image pixel for the task of background subtraction[J]. Pattern Recognition Letters, 2006, 27(7): 773-780. doi: 10.1016/j.patrec.2005.11.005
    [6]
    LEE D S. Effective Gaussian mixture learning for video background subtraction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5): 827-832. doi: 10.1109/TPAMI.2005.102
    [7]
    FENG Hua-wen, GONG Sheng-rong, LIU Chun-ping. Foreground detection based on improved Gaussian mixture model[J]. Computer Engineering, 2011, 37(19): 179-182. (in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-JSJC201119061.htm
    [8]
    LIU Jing, WANG Ling. Improved algorithm of Gaussian mixture model for background subtraction[J]. Computer Engineering and Applications, 2010, 46(13): 168-170. (in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201013052.htm
    [9]
    LI Hong-bo, TANG Pei-jun, WU Yu. Mixture Gaussian background modeling improved by Kalman filtering[J]. Computer Engineering and Applications, 2009, 45(24): 162-164, 245. (in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-JSGG200924050.htm
    [10]
    CHEN Zu-jue, CHEN Xiao-jun, HE Hong. Moving object detection based on improved mixture Gaussian models[J]. Journal of Image and Graphics, 2007, 12(9): 1585-1589. (in Chinese). doi: 10.3969/j.issn.1006-8961.2007.09.014
    [11]
    SEKI M, WADA T, FUJIWARA H, et al. Background subtraction based on cooccurrence of image variations[C]∥IEEE. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington DC: IEEE, 2003: 65-72.
    [12]
    HEIKKILA M, PIETIKAINEN M. A texture-based method for modeling the background and detecting moving objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 657-662.
    [13]
    CHEN Y T, CHEN C S, HUANG C R, et al. Efficient hierarchical method for background subtraction[J]. Patten Recognition, 2006, 40(10): 2706-2715.
    [14]
    CHANG Xiao-fu, ZHANG Wen-sheng, DONG Wei-shan. Mixture of Gaussian background modeling method based on multi-category visual features[J]. Journal of Image and Graphics, 2011, 16(5): 829-834. (in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201105018.htm
    [15]
    MASON M, DURIC Z. Using histograms to detect and track objects in color video[C]∥IEEE. Proceedings of the Applied Imagery Pattern Recognition Workshop. New York: IEEE, 2001: 154-159.
    [16]
    NORIEGA P, BASCLE B, BERNIER O. Local kernel color histograms for background subtraction[C]∥ALPESH R, HELDER A, BRUNO E. Proceedings of the First International Conference on Computer Vision Theory and Applications. Setubal: INSTICC, 2006: 213-219.
    [17]
    TSAI T, HUANG Y L, CHIANG T. Image retrieval based on dominant texture features[C]∥IEEE. Proceedings of the IEEE International Symposium on Industrial Electronics. New York: IEEE, 2006: 441-446.
    [18]
    WANG Yong-zhong, LIANG Yan, PAN Quan, et al. Spatiotemporal background modeling based on adaptive mixture of Gaussians[J]. Acta Automatica Sinica, 2009, 35(4): 371-378. (in Chinese). http://www.cnki.com.cn/Article/CJFDTOTAL-MOTO200904008.htm
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