吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (4): 1325-1329.doi: 10.13229/j.cnki.jdxbgxb201604044

• Orginal Article • Previous Articles     Next Articles

Moving object detection algorithm based on frame difference and background subtraction

SUN Ting1, 2, QI Ying-chun2, GENG Guo-hua1   

  1. 1.Institute of Visualization Technology,Northwest University,Xi'an 710069,China;
    2.School of Computer Science and Technology,Zhoukou Normal University,Zhoukou 466001,China
  • Received:2015-04-21 Online:2016-07-20 Published:2016-07-20

Abstract: In order to overcome the defects of frame difference and background subtraction algorithms in moving object detection, a new method that integrates the two algorithms is proposed. First, when the Gaussian mixture model is employed to build the background model, the update of the variance is modified, so the model is more close to the real background. Then, two consecutive frame differencing is replaced by three consecutive frame differencing, and the differencing threshold is adaptive. Finally, the results of the two differences are integrated and the target is extracted after morphological process. Experimental results show that the method can effectively suppress noise and the "hole", the method is more adaptable and has good detection results.

Key words: communications, frame difference, background subtraction, object detection, integration

CLC Number: 

  • TN91
[1] 汪冲,席志红,肖春丽. 基于背景差分的运动目标检测方法[J]. 应用科技,2009,36(10):16-18.
Wang Chong, Xi Zhi-hong, Xiao Chun-li. Moving objects detection based on background subtraction method[J]. Applied Science and Technology,2009,36(10):16-18.
[2] Piccardi M. Background subtraction techniques: a review[C]∥IEEE International Conference on Systems, Man and Cybemeties, Sydney, Australia,2004:3099-3104.
[3] 王振亚,曾黄麟. 一种基于帧间差分和光流技术结合的运动车辆检测和跟踪新算法[J]. 计算机应用与软件,2012,29(5):117-120.
Wang Zhen-ya, Zeng Huang-lin. A new algorithm of moving vehicle detection and tracking based on combining frame difference method with optical flow technique[J]. Computer Applications and Software,2012,29(5):117-120.
[4] Tsai D M, Lai S C. Independent component analysis-based background subtraction for indoor surveillance[J]. IEEE Transactions on Image Processing, 2009, 18(1):158-160.
[5] Zhang Jie-yu, Barron J L. Optical flow at occlusion[C]∥The Ninth Conference on Computer and Robot Vision, Toronto, Canada, 2012: 198-205.
[6] 胡觉晖, 李一民, 潘晓露. 改进的光流法用于车辆识别与跟踪[J].科学技术与工程,2010, 10(23):5814-5817.
Hu Jue-hui, Li Yi-min, Pan Xiao-lu. An improved optical flow algorithmin in vehicle identification and tracking[J]. Science Technology and Engineering,2010, 10(23):5814-5817.
[7] Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking[C]∥IEEE Computer Vision and Pattern Recognition,Fort Collins, USA, 1999: 246-250.
[8] Zhang J, Chen C H. Moving objects detection and segmentation in dynamic video backgrounds[C]∥IEEE Conference on Technologies for Homeland Security, Woburn, MA,2007:64-69.
[9] 袁国武, 陈志强, 龚健,等. 一种结合光流法与三帧差分法的运动目标检测算法[J]. 小型微型计算机系统,2013, 34(3):669-671.
Yuan Guo-wu, Chen Zhi-qiang,Gong Jian,et al. A moving object detection algorithm based on a combination of optical flow and three-frame difference[J]. Journal of Chinese Computer Systems,2013, 34(3):6 69-671.
[10] 陈俊超, 张俊豪, 刘诗佳,等. 基于背景建模与帧间差分的目标检测改进算法[J]. 计算机工程,2011, 37(增刊):171-173.
Chen Jun-chao, Zhang Jun-hao, Liu Shi-jia,et al. Improved target detection algorithm based on background modeling and frame difference[J]. Computer Engineering,2011, 37(Sup.):171-173.
[11] 王小平,张丽杰,常佶. 基于单高斯背景模型运动目标检测方法的改进[J]. 计算机工程与应用,2009,45(21):118-120.
Wang Xiao-ping, Zhang Li-jie, Chang Ji. Improved method of moving objects detection based on single-gaussian background mode[J]. Computer Engineering and Applications,2009,45(21):118-120.
[12] 魏晓慧,李良福,钱钧. 基于混合高斯模型的运动目标检测方法研究[J]. 应用光学,2010, 31(4):574-578.
Wei Xiao-hui, Li Liang-fu, Qian Jun. Moving object detection based on mixture Gaussian mode[J]. Journal of Applied Optics,2010, 31(4):574-578.
[13] 张俊根, 姬红兵. 高斯混合粒子PHD滤波被动测角多目标跟踪[J]. 控制与决策,2011, 26(3):413-417.
Zhang Jun-gen, Ji Hong-bing. Gaussian mixture particle probability hypothesis density based passive bearings-only multi-target tracking[J]. Control and Decision,2011, 26(3):413-417.
[14] Li Li, Xu Ji-ning. Moving human detection algorithm based on Gaussian mixture model[C]∥Proceedings of the 29th Chinese Control Conference, Beijing,2010:2853-2856.
[15] Xu L Q,Landabaso J L, Lei B. Segmentation and tracking of multiple moving objects for intelligent video analysis[J]. BT Technology Journal, 2004, 22(3):140-149.
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