计算机科学 ›› 2017, Vol. 44 ›› Issue (12): 304-309.doi: 10.11896/j.issn.1002-137X.2017.12.055

• 图形图像与模式识别 • 上一篇    下一篇

基于熵和相关接近度的混合高斯目标检测算法

李睿,盛超   

  1. 兰州理工大学计算机与通信学院 兰州730050,兰州理工大学计算机与通信学院 兰州730050
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(61263019)资助

Mixed Gaussian Target Detection Algorithm Based on Entropy and Related Close Degree

LI Rui and SHENG Chao   

  • Online:2018-12-01 Published:2018-12-01

摘要: 针对固定模型个数的混合高斯模型的背景建模速度慢和运动目标的拖影问题,提出了一种基于Tsallis熵和相关接近度的改进混合高斯算法。该算法利用Tsallis熵对高斯模型自适应地选择模型个数,加速背景建模;对于模型匹配判断条件,不能很好地体现相邻像素点的空间相关性的情况,提出了相关接近度作为模型更新的限定条件,以去除拖影。实验结果表明,改进的算法在实时性、检测正确率方面都有较好的改进。

关键词: 混合高斯模型,Tsallis熵,相关接近度,拖影

Abstract: Aiming at that the background modeling of the hybrid gaussian model with fixed model number is slow and the detected moving targets have following contour when they move,an imprvoed moving object detection method based on mixture gaussian model with Tsallis entropy and related close degree was proposed.The improved algorithm automatically chooses model numbers to accelerate the background modeling.For model matching judgment condition cannot reflect spatial correlation of adjacent pixels, this paper proposed the conception of related close degree as another qualification condition to remove following contour.The experimental results show the improved algorithm greatly improves in real-time and detection accuracy.

Key words: Gaussian mixture model,Tsallis entropy,Related close degree,Following contour

[1] SU Z,WANG W,XU C.Optical correlation detection technology of moving target under low contrast environment [J].Chinese Journal of Scientific Instrument,2013,34(2):319-325.
[2] XIN Y,HOU J,DONG L,et al.A self-adaptive optical flowmethod for the moving object detection in the video sequences [J].Optik-International Journal for Light and Electron Optics,2014,125(19):5690-5694.
[3] YUAN G W,CHENG Z Q,GONG J,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):668-671.(in Chinese) 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):668-671.
[4] FAN J,WANG R,ZHANG L,et al.Image sequence segmentation based on 2D temporal entropic thresholding [J].Pattern Recognition Letters,1996,17(10):1101-1107.
[5] CHEN C H,LIANG,et al.Frame difference energy image for gait recognition with incomplete silhouettes [J].Pattern Recognition Letters,2009,30(11):977-984.
[6] DOLL R P,ZITNICK C L.Fast Edge Detection Using Structured Forests [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,37(8):1558-1570.
[7] CANNY J.A Computational Approach to Edge Detection-Readings in Computer Vision [J].IEEE Transactions on Pattern Analysis & Machine Intelligence ,1986,PAMI-8(6):679-698.
[8] LIU X,ZHAO G,YAO J,et al.Background Subtraction Based on Low-rank and Structured Sparse Decomposition [J].IEEE Transactions on Image Processing,2015,24(8):2502-2514.
[9] EBADI S E,ONES V G,IZQUIERDO E.Approximated Robust Principal Component Analysis for Improved General Scene Background Subt- raction [J].arXiv:1603.05875.
[10] STAUFFER C,GRIMSON W E L.Adaptive Background Mixture Models for Real-Time Tracking [C]∥IEEE Computer Society.1999:22-46.
[11] HUANG W L,FAN Y,LI H Z,et al.Improved mixture Gaus-sian algorithm[J].Computer Engineering and Design,2011,32(2):592-595.(in Chinese) 黄文丽,范勇,李绘卓,等.改进的混合高斯算法 [J].计算机工程与设计,2011,32(2):592-595.
[12] LIU W J,LI L.Moving objects detection algorithm of improved mixture Gaussian model based on entropy theory[J].Application Research of Computers,2015,32(7):2226-2229.(in Chinese) 刘万军,李琳.基于熵理论改进混合高斯模型的运动目标检测算法[J].计算机应用研究,2015,2(7):2226-2229.
[13] WANG B Z,HU Y,GUO Z T,et al.New method for mixtureGaussian background model and moving object detection based on wronskian function[J].Application Research of Computers,2016,33(12):1-5.(in Chinese) 王宝珠,胡洋,郭志涛,等.基于朗斯基函数的混合高斯模型运动目标检测 [J].计算机应用研究,2016,33(12):1-5.
[14] LI Y,FAN X P.A new image threshold segmentation algorithm[J].Computer Simulation,2008,25(1):229-232.(in Chinese) 黎燕,樊晓平.Renyi熵与Tsallis熵的等价关系 [J].计算机仿真,2008,25(1):229-232.
[15] PAL N R,PAL S K.Object-background segmentation using new definitions of entropy [J].Computers & Digital Techniques Iee Proceedings E,1989,136(4):284-295.

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