Analysis of Background Features in Moving Object Detection for Industrial Scenes
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    Abstract:

    As the increased demand of modern manufacturing industry for product quality, production efficiency, and operation safety, video monitoring technology has been used more and more widely, which requires better precision and higher speed for video processing technology. Detecting moving object is the foundation for understanding and analyzing the video. Therefore, the algorithm for detecting moving object under industry background has been a hot research topic. Nevertheless, as an important component of this algorithm, the background features used in the moving object detection technologies have not received enough attention. Therefore, in this study, we demonstrate how to utilize visualization technology to extract the background features from the given pixel data and existing algorithm and then visualize the related features and data, which will help to obtain the relationships among these background features in a more straightforward way so that we can further explore them. The relationships among these background features can also be used for guiding algorithm design.

    Reference
    [1] 代科学, 李国辉, 涂丹, 等. 监控视频运动目标检测减背景技术的研究现状和展望. 中国图象图形学报, 2006, 11(7):919-927.[DOI:10.11834/jig.200607158]
    [2] Benezeth Y, Jodoin PM, Emile B, et al. Comparative study of background subtraction algorithms. Journal of Electronic Imaging, 2010, 19(3):033003.[DOI:10.1117/1.3456695]
    [3] Brutzer S, Höferlin B, Heidemann G. Evaluation of background subtraction techniques for video surveillance. Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, CO, USA. 2011. 1937-1944.
    [4] Bouwmans T. Traditional and recent approaches in background modeling for foreground detection:An overview. Computer Science Review, 2014, 11-12:31-66.[DOI:10.1016/j.cosrev.2014.04.001]
    [5] Sobral A, Vacavant A. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Computer Vision and Image Understanding, 2014, 122:4-21.[DOI:10.1016/j.cviu.2013.12.005]
    [6] Goyette N, Jodoin PM, Porikli F, et al. Changedetection. net:A new change detection benchmark dataset. Proceedings of 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Providence, RI, USA. 2012. 1-8.
    [7] Hofmann M, Tiefenbacher P, Rigoll G. Background segmentation with feedback:The pixel-based adaptive segmenter. Proceedings of 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Providence, RI, USA. 2012. 38-43.
    [8] Van Droogenbroeck M, Paquot O. Background subtraction:Experiments and improvements for ViBe. Proceedings of 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, RI, USA. 2012. 32-37.
    [9] Schick A, Bäuml M, Stiefelhagen R. Improving foreground segmentations with probabilistic superpixel Markov random fields. Proceedings of 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, RI, USA. 2012. 27-31.
    [10] Wang R, Bunyak F, Seetharaman G, et al. Static and moving object detection using flux tensor with split Gaussian models. Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Columbus, OH, USA. 2014. 420-424.
    [11] St-Charles P, Bilodeau GA, Bergevin R. Flexible background subtraction with self-balanced local sensitivity. Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Columbus, OH, USA. 2014. 414-419.
    [12] Jodoin PM, Piérard S, Wang Y, et al. Overview and benchmarking of motion detection methods. In:Bouwmans T, Porikli F, Hoferlin B, et al., eds. Background Modeling and Foreground Detection for Video Surveillance. New York:CRC Press, 2014.
    [13] Heikkila M, Pietikainen M. A texture-based method for modeling the background and detecting moving objects. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2006, 28(4):657-662.[DOI:10.1109/TPAMI.2006.68]
    [14] Barnich O, Van Droogenbroeck M. ViBe:A universal background subtraction algorithm for video sequences. IEEE Trans. on Image Processing, 2011, 20(6):1709-1724.[DOI:10.1109/TIP.2010.2101613]
    [15] Kim K, Chalidabhongse TH, Harwood D, et al. Real-time foreground-background segmentation using codebook model. Real-Time Imaging, 2005, 11(3):172-185.[DOI:10.1016/j.rti.2004.12.004]
    [16] Bouwmans T, El Baf F, Vachon B. Background modeling using mixture of Gaussians for foreground detection-a survey. Recent Patents on Computer Science, 2008, 1(3):219-237.[DOI:10.2174/2213275910801030219]
    [17] 万缨, 韩毅, 卢汉清. 运动目标检测算法的探讨. 计算机仿真, 2006, 23(10):221-226.[DOI:10.3969/j.issn.1006-9348.2006.10.056]
    [18] 史志刚. 视频序列中运动目标检测与跟踪技术研究[硕士学位论文]. 西安:西安电子科技大学, 2014.
    [19] 高美凤, 刘娣. 分块帧差和背景差相融合的运动目标检测. 计算机应用研究, 2013, 30(1):299-302.[DOI:10.3969/j.issn.1001-3695.2013.01.077]
    [20] 王东方, 王玉德, 王景武. 基于改进的混合高斯模型的运动目标检测方法. 激光技术, 2014, 38(6):776-779.[DOI:10.7510/jgjs.issn.1001-3806.2014.06.011]
    [21] 宋雪桦, 陈瑜, 耿剑锋, 等. 基于改进的混合高斯背景模型的运动目标检测. 计算机工程与设计, 2010, 31(21):4646-4649.
    [22] 魏建猛, 陈松, 庞首颜. 改进的混合高斯模型视频运动目标检测算法. 重庆交通大学学报(自然科学版), 2013, 32(2):365-368.[DOI:10.3969/j.issn.1674-0696.2013.02.41]
    [23] Ji ZX, Huang YB, Xia Y, et al. A robust modified Gaussian mixture model with rough set for image segmentation. Neurocomputing, 2017, 266:550-565.[DOI:10.1016/j.neucom.2017.05.069]
    [24] Chen W, Tian YH, Wang YW, et al. Fixed-point Gaussian mixture model for analysis-friendly surveillance video coding. Computer Vision and Image Understanding, 2016, 142:65-79.[DOI:10.1016/j.cviu.2015.09.006]
    [25] Huang W, Liu L, Cui MJ, et al. A novel evaluation metric based on visual perception for moving target detection algorithm. Infrared Physics & Technology, 2016, 76:285-294.
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高庆,崔友昌.工业场景中背景特征的建模要素分析.计算机系统应用,2018,27(6):1-11

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  • Received:July 25,2017
  • Revised:August 15,2017
  • Online: May 29,2018
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