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Multi-Object Detection Using Modified GMM-Based Background Subtraction Technique

  • Rohini Chavan
  • S. R. Gengaje
  • Shilpa Gaikwad
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Detection of objects is the most important and challenging task in video surveillance system in order to track the object and to determine meaningful and suspicious activities in outdoor environment. In this paper, we have implemented novel approach as modified Gaussian mixture model (GMM) based object detection technique. The object detection performance is improved compared to original GMM by adaptively tuning its parameters to deal with the dynamic changes that occurred in the scene in outdoor environment. Proposed adaptive tuning approach significantly reduces the overload experimentations and minimizes the errors that occurred in empirical tuning traditional GMM technique. The performance of the proposed system is evaluated using open source database consisting of seven video sequences of critical background condition.

Keywords

Detection Gaussian mixture model Video surveillance system 

References

  1. 1.
    Hsieh J-W, Yu S-H, Chen Y-S (2006) An automatic traffic Surveillance system for vehicle tracking and classification. IEEE Trans Intell Transp Syst 7CrossRefGoogle Scholar
  2. 2.
    Chauhan AK, Krishan P (2013) Moving object tracking using Gaussian mixture model and optical flow. Int J Adv Res Comput Sci Softw Eng 3Google Scholar
  3. 3.
    Picardi M (2004) Background subtraction technique: a review. In: IEEE international conference on systems, man and cybermeticsGoogle Scholar
  4. 4.
    Viola P, Jones M (2005) Detecting Pedestrians using patterns of motion and appearance. Int J Comput Vision 63(2):153–161CrossRefGoogle Scholar
  5. 5.
    Bo W, Nevatia R (2007) Detection and tracking of multiple partially occluded Humans by Baysian combination of edgelet based part detector. Int J Comput Vision 75(2):247–266CrossRefGoogle Scholar
  6. 6.
    Ran Y, Weiss I (2007) Pedestrian Detecting via periodic motion analysis. Int J Comput Vision 71(2):143–160CrossRefGoogle Scholar
  7. 7.
    Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real time tracking. In: International conference on computer vision and pattern recognition 2Google Scholar
  8. 8.
    Cucchiaria R, Grana C, Piccardi M, Prati A (2003) Detecting moving objects, ghosts and shadows in video streams. IEEE Trans PAMI 25(10):1337–1342Google Scholar
  9. 9.
    Toyama K, Krumm J, Brumitt B (1999) Wallflower: Principles and practice of background maintenance. In: International conference of computer vision, pp 255–261Google Scholar
  10. 10.
    Zhang, LZ, Hou Z, Wang H, Tan M (2005) An adaptive mixture Gaussian background model with online background reconstruction and motion segmentation. ICIT, pp 23–27Google Scholar
  11. 11.
    White B, Shah M (2007) Automatically tuning background subtraction parameters using Particle swarm optimization. In: IEEE international conference on multimedia and Expo, China, pp 1826–1829Google Scholar
  12. 12.
    Harville M, Gordon G, Woodfill J (2001) Foreground segmentation using adaptive mixture models in color and depth. In: Proceeding of the IEEE workshop on detection and recognition of events in Video, CanadaGoogle Scholar
  13. 13.
    Elgammal A, Harwood D, Davis L (2000) Non parametric model for background subtraction. In: European conference on computer vision, pp 751–767Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Bharati Vidyapeeth University College of EngineeringPuneIndia
  2. 2.Walchand Institute of TechnologySolapurIndia

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