A New Moving Human Detection Method in Color Video Image


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Moving object detection is the basic of video applications such as computer vision, object recognition and tracking, surveillance security etc. Background subtraction and symmetrical differencing are the popular methods of motion detection. The main idea of them is to compare the current video frame with a specified background image or a background model or the next video frame. For background subtraction, the obtaining of initialization is crucial and many methods have been employed, so it is necessary to model background to adapt the changes of background. In this paper, the single gaussian modeling as the initialization background model combined with an improved linear alternate background updating method is proposed. And then, a novel moving human detection method which employs background subtraction and symmetrical differencing based on rgb color difference model is presented. The experimental results show that the detection method can detect moving human effectively and real-time.



Edited by:

Mohamed Othman




T. N. Wu et al., "A New Moving Human Detection Method in Color Video Image", Applied Mechanics and Materials, Vols. 229-231, pp. 1166-1170, 2012

Online since:

November 2012




[1] Jianxin Wu, Christopher Geyer and James M. Rehg: Real-Time Human Detection Using Contour Cues. Robot. Automat. (2011), p.860.

DOI: https://doi.org/10.1109/icra.2011.5980437

[2] Zhen Tang and Zhenjiang Miao: Fast Background Subtraction and Shadow Elimination Using improved Gaussian Mixture Model. Haptic. Audio. Visual. Environ. Garnes (2007), p.38.

DOI: https://doi.org/10.1109/have.2007.4371583

[3] Wang Weiqiang, Yang Jie and Gao Wen: Modeling Background and Segmenting Moving Objects from Compressed Video. Circuit. Syste. for Video. Tech. Vol. 18 (2008), p.670.

DOI: https://doi.org/10.1109/tcsvt.2008.918800

[4] Niu Lianqiang and Nan Jiang: A moving objects detection algorithm based on improved background subtraction. Intellige. Syst. Design. Appl. Vol. 3 (2008), p.604.

DOI: https://doi.org/10.1109/isda.2008.337

[5] Du-Ming Tsai and Shia-Chih Lai: Independent Component Analysis-Based Background Subtraction for Indoor Surveillance. Image Processing. Vol. 18 (2009), p.158.

DOI: https://doi.org/10.1109/tip.2008.2007558

[6] Guiming HE, Lingjuan LI and Zhentang JIA: submitted to Journal of DFD(2003).

[7] C. Stauffer and W. E. Grimson: Adaptive background mixture models for real-time tracking. Comput. Visio. Patt. Recog. Vol. 2 (1999), p.247.

[8] Chen Yuan, Yu Shengsheng and Sun Weiping, et al: Objects Detecting Based on Adaptive Background Models and Multiple Cues. Comput. Commu. Contr. Manag. Vol. 1 (2008), p.286.

DOI: https://doi.org/10.1109/cccm.2008.288

[9] McKenna S J, Jabri S and Duric Z, et al: submitted to Journal of Computer Vision and Image Understanding (2000).

[10] Remagnino P, Tan T and Baker K: submitted to Journal of Image and Vision Computing (1998).

[11] Jong Bae Kim and Hang Joon Kim: submitted to Journal of Pattern Recognition Letters (2003).