一种融合MeanShift聚类分析和卷积神经网络的Vibe++背景分割方法

刘子豪; 贾小军; 张素兰; 徐志玲; 张俊

doi:10.11959/j.issn.1000-0801.2021046

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一种融合MeanShift聚类分析和卷积神经网络的Vibe++背景分割方法

研究与开发 | 更新时间：2024-06-05

- 一种融合MeanShift聚类分析和卷积神经网络的Vibe++背景分割方法
- Vibe++ background segmentation method combining MeanShift clustering analysis and convolutional neural network
- 电信科学 2021年37卷第3期页码：133-145
- 作者机构：
  
  1. 嘉兴学院数理与信息工程学院，浙江嘉兴 314033
  2. 中国计量大学质量与安全工程学院，浙江杭州 310018
  3. 浙江大学生物系统工程与食品科学学院，浙江杭州 310058
- 作者简介：
  
  [ "刘子豪（1988- ），男，博士，嘉兴学院讲师、硕士生导师，主要研究方向为人工智能与图像处理、基于机器视觉的农产品无损检测。" ]
  [ "贾小军（1974- ），男，博士，嘉兴学院副教授、硕士生导师，主要研究方向为人工智能与图像处理。" ]
  [ "张素兰（1980- ），女，博士，嘉兴学院讲师，主要研究方向为三值光学计算机、系统结构、嵌入式系统。" ]
  [ "徐志玲（1966- ），女，中国计量大学教授，主要研究方向为计量检测成像技术研究与仪器开发、工业机器人检测系统研究与开发。" ]
  [ "张俊（1994- ），男，浙江大学生物系统工程与食品科学学院博士生，主要研究方向为基于谱图成像技术的农产品无损检测。" ]
- 基金信息：
  
  浙江省基础公益研究计划项目;Zhejiang Public Welfare Technology Research Project(LGG21F030013);浙江省基础公益研究计划项目;Zhejiang Public Welfare Technology Research Project(LGG20F010010);浙江省基础公益研究计划项目;Zhejiang Public Welfare Technology Research Project(LGG20F030006);嘉兴市公益计划项目;Jiaxing City Public Welfare Technology Application Research Project of Jiaxing Science and Technology Bureau(2020AY10009);嘉兴市公益计划项目;Jiaxing City Public Welfare Technology Application Research Project of Jiaxing Science and Technology Bureau(2018AY11008);嘉兴学院科研启动基金;Scientific Research Foundation of Jiaxing University(CD70519085)
- DOI：10.11959/j.issn.1000-0801.2021046
  中图分类号： TN391
- 网络出版日期：2021-03，
  
  纸质出版日期：2021-03-20
- 稿件说明：
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刘子豪, 贾小军, 张素兰, 等. 一种融合MeanShift聚类分析和卷积神经网络的Vibe++背景分割方法[J]. 电信科学, 2021,37(3):133-145.

Zihao LIU, Xiaojun JIA, Sulan ZHANG, et al. Vibe++ background segmentation method combining MeanShift clustering analysis and convolutional neural network[J]. Telecommunications science, 2021, 37(3): 133-145.
刘子豪, 贾小军, 张素兰, 等. 一种融合MeanShift聚类分析和卷积神经网络的Vibe++背景分割方法[J]. 电信科学, 2021,37(3):133-145. DOI： 10.11959/j.issn.1000-0801.2021046.

Zihao LIU, Xiaojun JIA, Sulan ZHANG, et al. Vibe++ background segmentation method combining MeanShift clustering analysis and convolutional neural network[J]. Telecommunications science, 2021, 37(3): 133-145. DOI： 10.11959/j.issn.1000-0801.2021046.

摘要

针对传统 Vibe+算法存在噪点和拖影分割错误率较高的问题，提出了一种改进的 Vibe+运动目标分割算法（Vibe++）。首先，通过对视频帧采用传统Vibe+算法处理获取二值图像，基于区域生长算法对结果图中各连通域标记，依据边界面积块差异获取面积筛选阈值，将低于阈值的连通区域视为噪点并删除；然后，引入 5 种不同核函数优化传统 MeanShift 聚类算法，并与卷积神经网络（CNN）进行顺序组合；最后，采用组合模型对已消除噪点图像中的拖影区、非拖影区和拖影边缘区分类，计算拖影区中每个像素点的坐标，定位拖影区并快速删除，获取分割结果。所提算法用于公开数据集的实验结果表明，其可取得 98%以上的分割准确率，具有较好的应用效果和较高的实用价值。

Abstract

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.

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references

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