计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 154-157.

• 模式识别与图像处理 • 上一篇    下一篇

一种基于阈值的自适应Vibe目标检测算法

王辉,宋建新   

  1. 南京邮电大学江苏省图像处理与图像通信重点实验室 南京210003,南京邮电大学江苏省图像处理与图像通信重点实验室 南京210003
  • 出版日期:2018-11-14 发布日期:2018-11-14

Threshold Based Adaptive Vibe Target Detection Algorithm

WANG Hui and SONG Jian-xin   

  • Online:2018-11-14 Published:2018-11-14

摘要: Vibe算法是一种高效的像素级背景建模算法。该算法在运动目标检测过程中无法快速抑制鬼影,同时不能根据前景运动快慢调整背景更新速度。针对这些问题,提出了一种基于阈值的自适应Vibe目标检测算法。当某像素点被Vibe模型判别为前景时,采用Otsu算法计算图像的分割阈值。根据阈值对该像素点进行再次判别抑制鬼影像素点,并重新初始化该像素点的背景模型。通过计算运动目标的质心差,改进的Vibe算法能够自适应地调整背景的更新速度。结果表明,与原Vibe算法相比,改进后的算法能够在更少的帧数内有效地抑制鬼影,更加准确地检测出前景目标。

Abstract: Vibe algorithm is an effective pixel level background modeling algorithm.During the moving object detection process,the Vibe algorithm can’t eliminate ghost quickly and change the update speed according to the change speed of foreground.To solve these problems,this paper proposed a threshold based adaptive Vibe target detection algorithm.When a pixel is judged as foreground by Vibe model,Otsu algorithm will be used to calculate the threshold of image segmentation.The pixel will be judged again to eliminate ghost pixel according to the threshold,and the background model of the pixel is reinitialized.According to calculate the change of the centroid of the moving object,the improved algorithm changes the update rate of the background adaptively.The results show that the proposed algorithm can effectively absorb the ghost in less frames and detect the foreground object more accurately than the original Vibe algorithm.

Key words: Vibe algorithm,Otsu algorithm,Ghost suppression,Target detection

[1] 张磊,傅志中,周岳平.基于HSV颜色空间和Vibe算法的运动目标检测[J].计算机工程与应用,2014(4):181-185
[2] Wren C R,Azarbayejani A,Darrell T,et al.Pfinder:Real-time tracking of the human body [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):780-785
[3] Barron J L,Fleet D J,Beauchemin S S.Performance of optical flow techniques [J].International journal of computer vision,1994,12(1):43-77
[4] 李刚,邱尚斌,林凌,等.基于背景差法和帧间差法的运动目标检测方法[J].仪器仪表学报,2006,27(8):961-964
[5] Stauffer C,Grimson W E L.Adaptive Background MixtureModels for Real-Time Tracking[C]∥Proc.Computer Vision and Pattern Recognition 1999(CVPR’99).June 1999
[6] Barnich O,Van Droogenbroeck M.iBe:A powerful random technique to estimate the background in video sequences[C]∥Proc.Int.Conf.Acoust.Speech Signal Process.Apr.2009:945-948
[7] 陈亮 陈晓竹,范振涛.基于 Vibe 的鬼影抑制算法[J].中国计量学院学报,2013,24(4):425-429
[8] Van Droogenbroeck M,Paquot O.Background subtraction:Experiments and improvements for ViBe [C]∥2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).IEEE,2012:32-37
[9] Barnich O,Van Droogenbroeck M.ViBe:A universal back-ground subtraction algorithm for video sequences [J].IEEE Transactions on Image Processing,2011,20(6):1709-1724
[10] Otsu N.A threshold select ion method from gray-level histo-grams [J].IEEE Transactions on System Man and Cybernetic,1979,9(1):62-66
[11] 闵华清,吕居美,罗荣华,等.基于 GMM 和 MRF 的自适应阴影检测[J].华南理工大学学报:自然科学版,2011,39(7):115-120
[12] 张德才,周春光,周强,等.基于轮廓的孔洞填充算法[J].吉林大学学报:理学版,2011,49(1):82-86

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