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An adaptive hybrid GMM for multiple human detection in crowd scenario

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Abstract

People gather together for myriad reasons and in an overcrowded region, human detection is a very challenging problem. Automated multiple human detection is one of the most active research fields of computer vision applications. It provides useful information for crowd monitoring and traffic controlling for human safety in public places. The conventional Gaussian mixture model, that has a fixed K values, is not enough for dynamically varying background and further foreground detection is a time consuming process. The automated multiple human detection algorithms are needed to deal with complex background and illumination change conditions of crowd scene. In this paper, an automated multiple human detection method using hybrid adaptive Gaussian mixture model is proposed to handle efficiently the complex background and illumination changes. The proposed hybrid algorithm utilizes spatiotemporal features, adaptive learning control, adaptively changing weights and an adaptive selection with number of K Gaussian components per pixel to withstand in complex background and different lighting conditions. The experimental results and performance measures demonstrate that the proposed hybrid method performs well for crowd scene. By using the proposed adaptive hybrid method, the multiple human detection rate has been improved from 90 % to 95 % and the computational time is reduced from 5s e c s to 2.5s e c s.

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References

  1. Bouwmans T (2014) Traditional and Recent approaches in Background Modeling for Foreground Detection: An Overview. Comput Sci Rev 11:31–66

    Article  MATH  Google Scholar 

  2. Bouwmans T, El Baf F, Vachon B (2008) Background Modelling using Mixture of Gaussians for Foreground Detection: A Survey. Recent Patents Comput Sci 1(3):219–237

    Article  Google Scholar 

  3. Eshel R, Moses Y (2008) Homography based multiple-camera detection and tracking of people in a dense crowd. In: Proceedings of the conference on computer vision and pattern recognition

  4. Fazli S, Pour HM, Bouzari H (2009) A Novel GMM-Based Motion Segmentation Method for Complex Background. The 5th IEEE-GCC Conference

  5. Ferryman J, Ellis AL (2014) Performance evaluation of crowd image analysis using the PET 2009 dataset. Pattern Recognit Lett 44:3–15

    Article  Google Scholar 

  6. Hu Z et al (2011) Selective Eigenbackgrounds method for background subtraction in crowd scenes. IEEE International Conference on image processing, pp 3277–3280

  7. Karpagavalli P, Ramprasad AV (2013) Estimating of the density of people and counting the number of people in crowded environment for human safety. In: IEEE international conference on communications and signal processing (ICCSP), pp 663–667

  8. Karpagavalli P, Ramprasad AV (2013) Human detection and segmentation in the crowd environment by combining APD with HLBD approaches. In: National conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG), pp 1–4

  9. Kentaro T, Krumm J, Brumitt B, Meyers B (1999) Principles and practice of background maintenance. In: Proceedings of the Seventh IEEE international conference on computer vision (ICCV99), 1:255261

  10. Kertesz C (2011) Texture-based foreground detection. Int J Signal Process Image Process Pattern Recognit 4:51–61

    Google Scholar 

  11. Kim Z (2008) Real time object tracking based on dynamic feature grouping with background subtraction. In: Proceedings of IEEE conference on computer vision pattern recognition, pp 1–8 (2008)

  12. Kim K et al (2005) Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11(3):172–185

    Article  Google Scholar 

  13. Ko T, Soatto S, Estrin D (2008) Background subtraction on distributions. In: Computer vision ECCV 2008, pp 276289. Springer

  14. Lee DS (2005) Effective gaussian mixture learning for video background subtraction. IEEE Trans Pattern Anal Mach, Intell 27(5):827–832

    Article  Google Scholar 

  15. Li HH, Chuang JH, Liu TL (2011) Regularized background adaptation: A novel learning rate control scheme for gaussian mixture modeling. IEEE Trans Image Process 3(20):822–836

    MathSciNet  Google Scholar 

  16. Mahadevan V, Vasconcelos N (2008) BGS in highly dynamic scenes. In: IEEE conference on computer vision and pattern recognition, pp 1–6

  17. Richard J, Radke S, AI-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 4(3):294–307

    MathSciNet  Google Scholar 

  18. Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Analysis and Mach Intell 22(8):747–757

    Article  Google Scholar 

  19. Sobral A, Vacavant A (2014) A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput Vis Image Underst 122:4–21

    Article  Google Scholar 

  20. Suo P, Wang Y (2008) An Improved Adaptive Background Modeling Algorithm Based on Gaussian Mixture Model. In: IEEE Proceedings of ICSP 2008

  21. Sun L, Sheng W, Liu Y (2015) Background modeling and its evaluation for complex scenes. Multimedia Tools Appl 74:3947–3966

    Article  Google Scholar 

  22. Tan R, Huo H, Qian J, Fang T (2006) Traffic video segmentation using adaptive-K Gaussian mixture model. In: The international workshop on intelligent computing (IWICPAS 2006), pp 125–134

  23. Tian Y, Wang Y, Hu Z, Huang T (2013) Selective Eigenbackground for background modeling and subtraction in crowded scenes. In: IEEE transactions on circuits and systems for video technology

  24. Van Droogenbroeck M, Paquot O (2012) Background Subtraction: Experiments and Improvements for ViBe, Change Detection Workshop (CDW)

  25. Wang H, Suter D (2006) A novel robust statistical method for background initialization and visual surveillance. In: Computer vision ACCV 2006, pp 328337. Springer

  26. Wu B, Nevatia R (2005) Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors. In: Proceedings IEEE international conference on computer vision, vol 1, pp 90–97

  27. Yao L, Ling M (2014) An improved mixture-of-Gaussians background model with frame difference and blob tracking in video stream. Sci World J 424050

  28. Zhao L, Davis LS (2005) Closely coupled object detection and segmentation. In: Proceedings IEEE international conference on computer vision, pp 454–461

  29. Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. In: Proceeding ICPR, 2004

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Acknowledgments

Research and computing facilities was provided by the K.L.N. College of Engineering, Tamil Nadu, India.

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  1. K.L.N College of Engineering, Anna University, Chennai, India

    Karpagavalli P. &  Ramprasad A. V.

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  1. Karpagavalli P.
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  2. Ramprasad A. V.
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Correspondence to Karpagavalli P..

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Karpagavalli P., Ramprasad A. V. An adaptive hybrid GMM for multiple human detection in crowd scenario. Multimed Tools Appl 76, 14129–14149 (2017). https://doi.org/10.1007/s11042-016-3777-4

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  • DOI: https://doi.org/10.1007/s11042-016-3777-4

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