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Research Article |

A Novel User-Friendly Application for Foreground Detection with Post-Processing in Surveillance Video Analytics

Author(s): Fancy Joy1 and Dr. V Vijayakumar2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 4

Publisher : FOREX Publication

Published : 25 December 2022

e-ISSN : 2347-470X

Page(s) : 1256-1261

DOI: https://doi.org/10.37391/IJEER.100477

Abstract

Detection of the object in the video is a primary task of all video-processing-based applications. It is one of the challenging areas in computer vision. The paper presents a novel MATLAB-based object detection application based on an improved Gaussian Mixture Model. Gaussian Mixture Model with post-processing applied here for segmentation of foreground from background. The application is divided into three modules pre-processing, detection and post-processing. The morphological gradient filter uses here for segmenting the foreground objects from the background. The proposed method was tested for various video sequences with challenges and proved that the approach performs well for the majority video sequences.

Keywords: Object Detection, GMM, Post-processing.




Fancy Joy*, Sri Ramakrishna College of Arts and Science, Coimbatore, India; Email: fancyjoy1@gmail.com

Dr. V Vijayakumar, Sri Ramakrishna College of Arts and Science, Coimbatore, India

    [1] Joy F., Vijayakumar V., “A State of Art Methodology for Real-Time Surveillance Video Denoising”, In: Smys S., Tavares J., Balas V., Iliyasu A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing,2020, vol 1108, pp.894-901.Springer, Cham. [Cross Ref]
    [2] Juan Zhao, Guihui Xie, Dongming Li and Mailing Song, “Improved GMM-based method for target detection”, The Institution of Engineering and Technology,2020, Vol.9, Issue.1, pp. 7-11.[Cross Ref]
    [3] Caixia Zhang and Qingyang Xu,”An Improved GMM based Video Foreground Separation”, The 31th Chinese Control and Decision Conference (2019 CCDC), 2019, pp.1371-1374.[Cross Ref]
    [4] Venkata Prasad.Va, Chandra Sekhar Rayia , Cheggoju Naveena,, Vishal R. satpute,” Object’s Action Detection using GMM Algorithm for Smart Visual Surveillance System”, International Conference on Robotics and Smart Manufacturing (RoSMa2018), Procedia Computer Science 133,2018,pp. 276–283.[Cross Ref]
    [5] Fancy Joy and V. Vijayakumar.,”A Review on Multiple Object Detection and Tracking in Smart City Video Analytics”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2018, Volume-8 Issue-2S2 December.[Cross Ref]
    [6] M.H Nguyen, T.L. Vuong, D.N. Nguyen, D.V. Nguyen, H. Le and T. Nguyen, “Moving object detection in compressed domain for high-resolution videos”, Proceedings of the Eighth International Symposium on Information and Communication Technology, 2017.[Cross Ref]
    [7] Victor-Emil Neagoe, Vlad Chirila-Berbentea, “Improved Gaussian mixture model with expectation-maximization for clustering of remote sensing imagery.” 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016, pp.3063–3065.[Cross Ref]
    [8] Han, G,”An Improved Gaussian Mixture Model and Its Application”, In: Wong, W. (eds) Proceedings of the 4th International Conference on Computer Engineering and Networks,Lecture Notes in Electrical Engineering, 2015,vol 355. Springer, Cham. [Cross Ref]
    [9] Y. Song, N. Fu, X. Li and Q. Liu, "Fast moving object detection using improved Gaussian mixture models," 2014 International Conference on Audio, Language and Image Processing, 2014, pp. 501-505.[Cross Ref]
    [10] Kumthekar, A. V., and J. K. Patil, "Key frame extraction using color histogram method." International Journal of Scientific Research Engineering & Technology ,2013, pp. 207-214.[Cross Ref]
    [11] Zhiguo Bian and Xiaoshu,” Moving object detection based on improved gaussian mixture model” 2012 5th international congress on image and signal processing (CISP 2012),2012.[Cross Ref]
    [12] LI-Yi Bing,JIA He-jiang, and LI Ao, ”A moving object detection method based on improved GMM “,Advanced materials research ,2012, vol.225-226,pp. 637-641.[Cross Ref]
    [13] Jinfu Yang, Wanlu Yang and Mingai Li,” An efficient moving object detection algorithm based on improved GMM and cropped frame technique”, Proceedings of 2012 IEEE International Conference on Mechatronics and Automation August 5 - 8, Chengdu, China,2012.[Cross Ref]
    [14] Yingying Zhu, Ye Liang and Yanyan Zhu,” The improved gaussian mixture model based on motion estimation”,2011 third international conference on multimedia information networking and security, IEEE,2011.[Cross Ref]
    [15] Chen, G., Yu, Z., Wen, Q., & Yu, Y.,” Improved Gaussian Mixture Model for Moving Object Detection”. Lecture Notes in Computer Science,2011, pp. 179–186.[Cross Ref]
    [16] S.Paris, P. Kornprobst, J Tumblin and F. Durand, “Bilateral filtering: Theory and applications“, Foundations and trends in Computer graphics and Vision, 2008, Vol.4, Issue.1, pp.1-73.[Cross Ref]
    [17] Chris Stauffer and W. Eric L. Grimson,” Adaptive background mixture models for real-time tracking,” Proceedings of the 1999 IEEE computer society conference on computer vision and pattern recognition, IEEE.Comput.Soc, 1999.Part.Vol.2.[Cross Ref]
    [18] http://www.changedetection.net/[Cross Ref]
    [19] http://cs.binghamton.edu/~mrldata/ pets2009[Cross Ref]

Fancy Joy and Dr. V Vijayakumar (2022), A Novel User-Friendly Application for Foreground Detection with Post-Processing in Surveillance Video Analytics. IJEER 10(4), 1256-1261. DOI: 10.37391/IJEER.100477.