基于改进视觉背景提取算法的运动目标检测方法
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摘要
针对视觉背景提取算法在复杂环境下检测出现鬼影现象、动态背景因素形成噪声干扰等问题,提出一种改进的视觉背景提取算法。通过创建辅助样本集,对复杂环境中像素点的重要特征信息进行收集;引入像素点鬼影因子和区域复杂度分析,自适应调节各像素点的匹配阈值和更新速率;最后通过基于滑动窗的像素点闪烁程度分析,将可能被误检为前景的噪声点向辅助样本中依概率更新。多场景下对比实验表明,该算法可将错分率降低至1.49%,且在检测时能快速消除鬼影现象,有效抑制动态背景产生的噪声干扰,同时保证前景目标能被完整识别,在复杂环境下的检测结果更加准确。
Aiming at the problems of ghost and the noise interference from dynamic background in classic visual background extraction algorithm,an improved visual background extraction algorithm is proposed.The important feature information of pixels in complex environment can be collected by creating the auxiliary sample set.By introducing analysis of the pixel ghost factor and the region complexity,the matching threshold and updating rate of each pixel can be adaptively adjusted.With the pixel flicker analysis based on sliding window,the points which may be misdetected as foreground can be updated to the auxiliary samples according to probability.The comparative experiments in the multi-scene show that the proposed method can reduce the wrong classifications rate to as low as1.49%,eliminate the ghost quickly,suppress the noise interference from the dynamic background,and ensure the complete recognition of foreground target.The results of the algorithm are more accurate in the complex environment.
Aiming at the problems of ghost and the noise interference from dynamic background in classic visual background extraction algorithm,an improved visual background extraction algorithm is proposed.The important feature information of pixels in complex environment can be collected by creating the auxiliary sample set.By introducing analysis of the pixel ghost factor and the region complexity,the matching threshold and updating rate of each pixel can be adaptively adjusted.With the pixel flicker analysis based on sliding window,the points which may be misdetected as foreground can be updated to the auxiliary samples according to probability.The comparative experiments in the multi-scene show that the proposed method can reduce the wrong classifications rate to as low as1.49%,eliminate the ghost quickly,suppress the noise interference from the dynamic background,and ensure the complete recognition of foreground target.The results of the algorithm are more accurate in the complex environment.
引文
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[2]Ding Q,Gu G H,Xu F Y,et al.Moving target detection on moving camera with the presence of strong parallax[J].Laser&Optoelectronics Progress,2015,52(9):091501.丁祺,顾国华,徐富元,等.强视差下的移动相机运动目标检测[J].激光与光电子学进展,2015,52(9):091501.
[3]Xu H W,Chen Q,Qian W X.Target detection algorithm based on improved single Gaussian background model[J].Laser&Optoelectronics Progress,2016,53(4):040401.徐鸿伟,陈钱,钱惟贤.基于改进的单高斯背景模型检测算法的研究[J].激光与光电子学进展,2016,53(4):040401.
[4]Wang X M,Zhao D Q,Sun G M,et al.Target detection algorithm based on improved Gaussian mixture model[C]∥Proceedings of the 2015International Conference on Electrical,Computer Engineering and Electronics,2015.
[5]Zhou J Y,Wu X P,Zhang C,et al.A moving object detection method based on sliding window Gaussian mixture model[J].Journal of Electronics&Information Technology,2013,35(7):1650-1656.周建英,吴小培,张超,等.基于滑动窗的混合高斯模型运动目标检测方法[J].电子与信息学报,2013,35(7):1650-1656.
[6]Godbehere A B,Matsukawa A,Goldberg K.Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation[C]∥2012 American Control Conference(ACC),2012:4305-4312.
[7]Zhang W Y,Xu H Z,Luo J.Moving objects detection under complex background based on ViBe[J].Computer Science,2017,44(9):304-307.张文雅,徐华中,罗杰.基于ViBe的复杂背景下的运动目标检测[J].计算机科学,2017,44(9):304-307.
[8]Xu J Q,Jiang P P,Zhu H B,et al.An improved ViBe algorithm for moving object detection[J].Journal of Northeastern University(Natural Science),2015,36(9):1227-1231.徐久强,江萍萍,朱宏博,等.面向运动目标检测的ViBe算法改进[J].东北大学学报(自然科学版),2015,36(9):1227-1231.
[9]Mo S W,Deng X P,Wang S,et al.Moving object detection algorithm based on improved visual background extractor[J].Acta Optica Sinica,2016,36(6):0615001.莫邵文,邓新蒲,王帅,等.基于改进视觉背景提取的运动目标检测算法[J].光学学报,2016,36(6):0615001.
[10]Xie S R,Ye S B,Yang B H,et al.Moving targets detection based on an improved YUV_Vibe fusion algorithm[J].Laser&Optoelectronics Progress,2018,55(11):111002.谢申汝,叶生波,杨宝华,等.基于改进的YUV_Vibe融合算法的运动目标检测[J].激光与光电子学进展,2018,55(11):111002.
[11]van Droogenbroeck M,Paquot O.Background subtraction:experiments and improvements for ViBe[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops,2012:32-37.
[12]Barnich O,van Droogenbroeck M.ViBe:a universal background subtraction algorithm for video sequences[J].IEEE Transactions on Image Processing,2011,20(6):1709-1724.
[13]Goyette N,Jodoin P M,Porikli F,et al.Changedetection.net:a new change detection benchmark dataset[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops,2012:1-8.