Telecommunications Science ›› 2021, Vol. 37 ›› Issue (3): 133-145.doi: 10.11959/j.issn.1000-0801.2021046

• Research and Development • Previous Articles     Next Articles

Vibe++ background segmentation method combining MeanShift clustering analysis and convolutional neural network

Zihao LIU1, Xiaojun JIA1, Sulan ZHANG1, Zhiling XU2, Jun ZHANG3   

  1. 1 College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314033, China
    2 College of Quality &Safty Engineering, China Jiliang University, Hangzhou 310018, China
    3 College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
  • Revised:2021-02-17 Online:2021-03-20 Published:2021-03-01
  • Supported by:
    Zhejiang Public Welfare Technology Research Project(LGG21F030013);Zhejiang Public Welfare Technology Research Project(LGG20F010010);Zhejiang Public Welfare Technology Research Project(LGG20F030006);Jiaxing City Public Welfare Technology Application Research Project of Jiaxing Science and Technology Bureau(2020AY10009);Jiaxing City Public Welfare Technology Application Research Project of Jiaxing Science and Technology Bureau(2018AY11008);Scientific Research Foundation of Jiaxing University(CD70519085)


To solve problems of noise points and high segmentation error for image shadow brought by traditional Vibe+ algorithm, a novel background segmentation method (Vibe++) based on the improved Vibe+ was proposed.Firstly, binarization image was acquired by using traditional Vibe+ algorithm from surveillance video.The connected regions were marked based on the region-growing domain marker method.The area threshold was obtained with difference characteristics of boundary area, the connected regions below threshold were treated as disturbing points.Secondly, five different kernel functions were introduced to improve the traditional MeanShift clustering algorithm.After improving, this algorithm was fused effectively with partitioned convolutional neural network.Finally, program of classification of trailing area, non-trailing area and trailing edge area in the resulting image was performed.Position coordinates of the trailing area were calculated and confirmed, and the trailing area was quickly deleted to obtain the final segmentation result.This segmentation accuracy was greatly improved by using the proposed method.The experimental results show that the proposed algorithm can achieve segmentation accuracy of more than 98% and has good application effect and high practical value.

Key words: background segmentation, clustering analysis, segmentation accuracy, convolutional neural network

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