一种融合MeanShift聚类分析和卷积神经网络的Vibe++背景分割方法

doi:10.11959/j.issn.1000-0801.2021046

Abstract

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.

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

CLC Number:

TN391

Zihao LIU, Xiaojun JIA, Sulan ZHANG, Zhiling XU, Jun ZHANG. Vibe++ background segmentation method combining MeanShift clustering analysis and convolutional neural network[J]. Telecommunications Science, 2021, 37(3): 133-145.

Figures/Tables 11

分割算法	算法性能评价指标
分割算法	视频帧分割时长/ms	分割准确率	算法时间复杂度
背景差分法^[1]	23.5	83.31%	O(n)
光流法^[2]	86.9	93.12%	O(nlgn)
分水岭算法^[3]	56.9	89.78%	O(lgn)
帧差法^[4]	$12 . 4$	85.72%	O(n)
水平集法^[5]	42.1	87.93%	O(n²)
Vibe^[6]	89.4	92.13%	O(m×n)
Vibe+^[7]	107.6	95.45%	O(nlg(mn))
背景更新恢复法^[8]	34.5	86.05%	O(n³)
Vibe++	132.7	$98 . 58 %$	O(n²lg(mn²))

对比的数据集类型	所提方法相比于之前方法的分割准确率
对比的数据集类型	Previous best	Vibe+	Vibe++
基准类	$95 . 32 %$ ^[22]	92.62%	94.89%
动态背景类	92.12%^[6]	72.91%	$94 . 32 %$
抖动类	95.16%^[6]	80.64%	$98 . 05 %$
间断运动目标类	$96 . 62 %$ ^[23]	75.12%	95.98%
拖影类	94.72%^[6]	83.02%	$97 . 68 %$
热图类	$98 . 29 %$ ^[24]	93.63%	98.16%
所有类（Overall）	97.36%^[6]	87.45%	$98 . 58 %$

References 27

[1]	DIM M , BASHIR A , BASIT A ,et al. Abandoned object detection using frame differencing and background subtraction[J]. International Journal of Applied Mathematics and Computer Science, 2020,11(7): 676-681.
[2]	ZHOU S T , ZHANG Q , GAO R ,et al. High-accuracy atmospheric turbulence compensation based on a Wirtinger flow algorithm in an orbital angular momentum-free space optical communication system[J]. Optical Communication, 2020,477(2): 345-353.
[3]	DERIVAUX S , FORESTIER G , WEMMERT C ,et al. Supervised image segmentation using watershed transform,fuzzy classification and evolutionary computation[J]. Pattern Recogntion Letters, 2010,31(15): 2364-2374.
[4]	ALGETHAMI N , REDFERN S . A robust tracking-by-detection algorithm using adaptive accumulated frame differencing and corner Features[J]. Journal of Imaging, 2020,6(4): 1-10.
[5]	LYRAS K G , HANSON B , FAIRWEATHER M ,et al. A coupled level set and volume of fluid method with a re-initialisation step suitable for unstructured meshes[J]. Journal of Computation Physics, 2020,407(12): 124-139.
[6]	ALVAREZ-MEZA A M , MOLINA-GIRALDO S , CASTELLANOS-DOMINGUEZ G . Background modeling using object-based selective updating and correntropy adaptation[J]. Image and Vision Computing, 2016,45(13): 22-36.
[7]	BARNICH O , VAN D M . ViBe:a universal background subtraction algorithm for video sequences[J]. IEEE Transaction of Image Process, 2011,20(6): 1709-1724.
[8]	GULESIR G , BERGMANS L , AKSIT M ,et al. Vibes:a visual language for specifying behavioral requirements of algorithms[J]. Journal of Visual Languages ＆ Computing, 2013,24(5): 350-364.
[9]	ZHOU X Q , LIU X F , JIANG A M ,et al. Improving video segmentation by fusing depth cues and the visual background extractor (ViBe) algorithm[J]. Sensors, 2017,17(5): 56-67.
[10]	彭丰平, 鲍苏苏, 曾碧卿 . 基于自适应区域生长算法的肝脏分割[J]. 计算机工程与应用, 2010,46(33): 198-200.
	PENG F P , BAO S S , ZENG B Q . Segmentation of liver based on adaptive region growing[J]. Computer Engineering and Applications, 2010,46(33): 198-200.
[11]	钮圣唬, 王盛, 杨晶晶 ,等. 完全基于边缘信息的快速图像分割算法[J]. 计算机辅助设计与图形学学报, 2012,24(11): 1410-1419.
	NIU S X , WANG S , YANG J J ,et al. A fast segmentation algorithm fully based on edge information[J]. Journal of Computer-Aided Design ＆ Computer Graphics, 2012,24(11): 1410-1419.
[12]	CAO X F , QIU B Z , XU G D . BorderShift:toward optimal MeanShift vector for cluster boundary detection in high-dimensional data[J]. Pattern Analysis and Applications, 2019,22(3): 1015-1027.
[13]	MITTAL G , KORUS P , MEMON N . FiFTy:large-scale file fragment type identification using convolutional nural networks[J]. IEEE Transactions on Information Forensics and Security, 2021,16(3): 28-41.
[14]	KEINOSUKE F , LARRY D H . The estimation of the gradient of a density function with application in pattern recognition[J]. IEEE Transations on Information Theory, 1975,21(1): 32-40.
[15]	CHENG Y Z . Mean shift,mode seeking,and clustering[J]. IEEE Transations on Pattern and Intelligence, 1995,17(8): 790-799.
[16]	姜枫, 顾庆, 郝慧珍 ,等. 基于内容的图像分割方法综述[J]. 软件学报, 2017,28(1): 160-183.
	JIANG F , GU Q , HAO H Z ,et al. Survey on content-based image segmentation methods[J]. Journal of Software, 2017,28(1): 160-183.
[17]	LIU Z , CHENG F , HONG HM . Identification of impurities in fresh shrimp using improved majority scheme-based classifier[J]. Food Analytical Methods, 2016,16(3): 10-20.
[18]	任洪蛾, 白杰云 . Lab颜色空间中基于动态聚类的颜色分级[J]. 计算机工程, 2013,39(6): 231-235.
	REN H E , BAI J Y . Color grading based on dynamic clustering in lab color space[J]. Computer Engineering, 2013,39(6): 231-235.
[19]	彭宁嵩, 杨杰, 刘志 ,等. MeanShift 跟踪算法中核函数窗宽的自动选取[J]. 软件学报, 2005,16(9): 1542-1550.
	PENG N S , YANG J , LIU Z ,et al. Automatic selection of kernel-bandwidth for Mean-Shift object tracking[J]. Journal of Software, 2005,16(9): 1542-1550.
[20]	邓柳, 汪子杰 . 基于深度卷积神经网络的车型识别研究[J]. 计算机应用研究, 2016,33(3): 930-932.
	DENG L , WANG Z J . Deep convolution neural networks for vehicle classification[J]. Application Research of Computers, 2016,33(3): 930-932.
[21]	DROOGENBROECK M V , PAQUOT O . Background subtraction:experiments and improvements for ViBe[C]// Proceedings:In Change Detection Workshop(CDW).[S.l.:s.n.], 2012.
[22]	HOFMANN M , TIEFENBACHER P,RIGOLL,G . Background segmentation with feedback:The pixel-based adaptive segmenter[C]// Proceedings of Computer Vision and Pattern Recognition Workshops(CVPR). Piscataway:IEEE Press, 2012: 38-43.
[23]	GOYETTE N , JODOIN P , PORIKLI F ,et al. Changedetection net:A new change detection benchmark dataset[C]// Proceedings of Computer Vision and Pattern Recognition Workshops(CVPR). Piscataway:IEEE Press, 2012.
[24]	CHEN Z , ELLIS T . A self-adaptive gaussian mixture model[J]. Computer Vision and Image Understanding, 2014(122): 35-46.
[25]	何利, 李伯全 . 复杂背景中奶粉罐内配件数量检测研究[J]. 机电工程, 2014,31(8): 1098-1102.
	HE L , LI B Q . Detection of the number of accessories in the milk powder cans in complex background[J]. Journal of Mechanical ＆ Electrical Engineering, 2014,31(8): 1098-1102.
[26]	周文静, 查志华, 吴杰 . 改进圆形 Hough 变换的田间红提葡萄果穗成熟度判别[J]. 农业工程学报, 2020,36(9): 205-213.
	ZHOU W J , ZHA Z H , WU J . Maturity discrimination of “Red Globe” grape cluster in grapery by improved circle hough transform[C]// Proceedings of Transactions of the Chinese Society of Agriculture Engineering(Transactions of the ASABE).[S.l.:s.n.], 2020,36(9): 205-213.
[27]	LIU Z H , CHENG F , ZHANG W . Recognition-based image segmentation of touching pairs of cooked shrimp (Penaeus Orientalis) using improved pruning algorithm for quality measurement[J]. Journal of Food Engineering, 2017,195(6): 166-181.

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序号	CNN结构变更	分割准确率	分割时间/ms
1	删除FC1	98.51%	118.91
2	删除FC2	78.32%	103.54
3	删除Pooling1	97.45%	124.76
4	删除Pooling2	97.28%	128.54
3	只保留Conv1	68.25%	73.75
4	只保留Conv2	69.36%	69.82
5	只保留Conv1+Conv2	83.48%	83.24
6	删除Conv2+Pooling2	85.31%	112.42
7	删除Conv1+Pooling1	87.37%	108.31
8	删除Conv5	90.23%	122.13
9	删除Conv6	91.46%	124.54
	删除Conv5+Conv6	89.79	101.85
10	调整Conv1,2 卷积核数量32,64	95.14%	98.73
11	调整Conv1,2 卷积核数量64,128	96.58%	114.54
12 (基准)	调整Conv1,2 卷积核数量128,256	98.58%	132.77
13	调整Conv1,2 卷积核数量256,512	97.32%	156.48

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

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