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Dynamic background modeling using intensity and orientation distribution of video sequence

  • Rhittwikraj MoudgollyaEmail author
  • Abhishek Midya
  • Arun Kumar Sunaniya
  • Jayasree Chakraborty
Article
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Abstract

Moving object detection in a video sequence is a challenging task in presence of dynamic background. In this paper, we propose a novel approach for background modeling by exploiting orientated patterns present in a video scene. Based on the observation that there exists a difference in directional edge patterns between foreground and background, we use the statistical measures of the orientation of texture via two angle co-occurrence matrices (ACMs). Orientation based features extracted from ACMs are then clubbed with intensity distribution-based features extracted from well-known gray level co-occurrence matrix (GLCM) to model the dynamic background. The model is then used to classify pixels within a video frame into background and foreground. Experimental results on a diverse set of video sequences have shown the effectiveness of the proposed method over competing schemes.

Keywords

Texture feature Dynamic background Gray level co-occurrence matrix Angle co-occurrence matrix Background modeling 

Notes

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
    Berjon D, Cuevas C, Moran F, Garcia N (2013) Gpu-based implementation of an optimized nonparametric background modeling for real-time moving object detection. IEEE Trans Consum Electron 59(2):361–369CrossRefGoogle Scholar
  6. 6.
    Bouwmans T (2011) Recent advanced statistical background modeling for foreground detection-a systematic survey. Recent Patents on Computer Science 4(3):147–176Google Scholar
  7. 7.
    Bouwmans T (2014) Traditional and recent approaches in background modeling for foreground detection: an overview. Computer Science Review 11:31–66CrossRefGoogle Scholar
  8. 8.
    Butler DE, Bove VM, Sridharan S (2005) Real-time adaptive foreground/background segmentation. EURASIP Journal on Advances in Signal Processing 2005(14):841,926CrossRefGoogle Scholar
  9. 9.
    Candès EJ, Li X, Ma Y, Wright J (2011) Robust principal component analysis. J ACM (JACM) 58(3):11MathSciNetCrossRefGoogle Scholar
  10. 10.
    Chakraborty J, Midya A, Rabidas R (2018) Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns. Expert Syst Appl 99:168–179CrossRefGoogle Scholar
  11. 11.
    Chakraborty J, Rangayyan RM, Banik S, Mukhopadhyay S, Desautels JEL (2012) Detection of architectural distortion in prior mammograms using statistical measures of orientation of textureGoogle Scholar
  12. 12.
    Chakraborty J, Rangayyan RM, Banik S, Mukhopadhyay S, Desautels JL (2012) Statistical measures of orientation of texture for the detection of architectural distortion in prior mammograms of interval-cancer. J Electron Imaging 21(3):033,010–1CrossRefGoogle Scholar
  13. 13.
    Cheung SCS, Kamath C (2005) Robust background subtraction with foreground validation for urban traffic video. EURASIP Journal on Advances in Signal Processing 2005(14):726,261CrossRefGoogle Scholar
  14. 14.
    Chiranjeevi P, Sengupta S (2011) Moving object detection in the presence of dynamic backgrounds using intensity and textural features. J Electron Imaging 20(4):043,009–043,009CrossRefGoogle Scholar
  15. 15.
    Chiranjeevi P, Sengupta S (2012) New fuzzy texture features for robust detection of moving objects. IEEE Signal Process Lett 19(10):603–606CrossRefGoogle Scholar
  16. 16.
    Chiranjeevi P, Sengupta S (2014) Detection of moving objects using multi-channel kernel fuzzy correlogram based background subtraction. IEEE Transactions on Cybernetics 44(6):870–881CrossRefGoogle Scholar
  17. 17.
    Culibrk D, Marques O, Socek D, Kalva H, Furht B (2007) Neural network approach to background modeling for video object segmentation. IEEE Trans Neural Netw 18(6):1614–1627CrossRefGoogle Scholar
  18. 18.
    Elgammal A, Duraiswami R, Harwood D, Davis LS (2002) Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc IEEE 90(7):1151–1163CrossRefGoogle Scholar
  19. 19.
    Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. Computer Vision ECCV 2000, pp 751–767Google Scholar
  20. 20.
    Farcas D, Marghes C, Bouwmans T (2012) Background subtraction via incremental maximum margin criterion: a discriminative subspace approach. Mach Vis Appl 23(6):1083–1101CrossRefGoogle Scholar
  21. 21.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621CrossRefGoogle Scholar
  22. 22.
    Heikkila M, Pietikainen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28 (4):657–662CrossRefGoogle Scholar
  23. 23.
    Hsia CH, Guo JM (2014) Efficient modified directional lifting-based discrete wavelet transform for moving object detection. Signal Process 96:138–152CrossRefGoogle Scholar
  24. 24.
    Huang J, Zhang T, Metaxas D (2011) Learning with structured sparsity. J Mach Learn Res 12(Nov):3371–3412MathSciNetzbMATHGoogle Scholar
  25. 25.
    Jalal AS, Singh V (2014) A framework for background modelling and shadow suppression for moving object detection in complex wavelet domain. Multimed Tools Appl 73(2):779–801CrossRefGoogle Scholar
  26. 26.
    Javed S, Mahmood A, Bouwmans T, Jung SK (2016) Spatiotemporal low-rank modeling for complex scene background initialization. IEEE Transactions on Circuits and Systems for Video TechnologyGoogle Scholar
  27. 27.
    Jodoin PM, Maddalena L, Petrosino A, Wang Y (2017) Extensive benchmark and survey of modeling methods for scene background initialization. IEEE Trans Image Process 26(11):5244–5256MathSciNetCrossRefGoogle Scholar
  28. 28.
    Jodoin PM, Mignotte M, Konrad J (2007) Statistical background subtraction using spatial cues. IEEE Trans Circuits Syst Video Technol 17(12):1758–1763CrossRefGoogle Scholar
  29. 29.
    Karasulu B, Korukoglu S (2013) Moving object detection and tracking in videos. In: Performance Evaluation Software, pp 7–30Google Scholar
  30. 30.
    Karpagavalli P, Ramprasad A (2017) An adaptive hybrid gmm for multiple human detection in crowd scenario. Multimed Tools Appl 76(12):14,129–14,149CrossRefGoogle Scholar
  31. 31.
    Kim H, Sakamoto R, Kitahara I, Toriyama T, Kogure K (2007) Robust foreground extraction technique using gaussian family model and multiple thresholds. In: Asian Conference on Computer Vision, pp 758–768Google Scholar
  32. 32.
    Kim K, Chalidabhongse TH, Harwood D, Davis L (2004) Background modeling and subtraction by codebook construction. In: IEEE International Conference on Image Processing, vol 5. pp 3061–3064Google Scholar
  33. 33.
    Kim K, Chalidabhongse TH, Harwood D, Davis L (2004) Background modeling and subtraction by codebook construction. In: Conference on Image Processing, vol 5. pp 3061–3064Google Scholar
  34. 34.
    Kim W, Kim C (2012) Background subtraction for dynamic texture scenes using fuzzy color histograms. IEEE Signal Process Lett 19(3):127–130CrossRefGoogle Scholar
  35. 35.
    Lee B, Hedley M (2002) Background estimation for video surveillance. In: Image and Vision Computing, pp 315–320Google Scholar
  36. 36.
    Lin HH, Liu TL, Chuang JH (2002) A probabilistic svm approach for background scene initialization. In: International Conference on Image Processing, vol 3. IEEE, pp 893–896Google Scholar
  37. 37.
    Lipton AJ, Fujiyoshi H, Patil RS (1998) Moving target classification and tracking from real-time video. In: Fourth IEEE Workshop on Aplications of Computer Vision, pp 8–14Google Scholar
  38. 38.
    Liu C, Yuen PC, Qiu G (2009) Object motion detection using information theoretic spatio-temporal saliency. Pattern Recogn 42(11):2897–2906CrossRefGoogle Scholar
  39. 39.
    Luque RM, López-rodríguez D, Merida-Casermeiro E, Palomo EJ (2008) Video object segmentation with multivalued neural networks. In: Eighth international conference on hybrid intelligent systems, IEEE, pp 613–618Google Scholar
  40. 40.
    Marghes C, Bouwmans T, Vasiu R (2012) Background modeling and foreground detection via a reconstructive and discriminative subspace learning approach. In: International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), p 1. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)Google Scholar
  41. 41.
    McIvor AM (2000) Background subtraction techniques. Proc of Image and Vision Computing 4:3099–3104Google Scholar
  42. 42.
    Midya A, Chakraborty J, Gönen M, Do RKG, Simpson AL (2018) Influence of ct acquisition and reconstruction parameters on radiomic feature reproducibility. J Med Imaging 5(1):5–5–15CrossRefGoogle Scholar
  43. 43.
    Pak LM, Chakraborty J, Gonen M, Chapman WC, Do RK, Koerkamp BG, Verhoef K, Lee SY, Massani M, van der Stok EP, Simpson AL (2018) Quantitative imaging features and postoperative hepatic insufficiency: A multi-institutional expanded cohort. Journal of the American College of SurgeonsGoogle Scholar
  44. 44.
    Paragios N, Deriche R (2000) Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans Pattern Anal Mach Intell 22 (3):266–280CrossRefGoogle Scholar
  45. 45.
    Seki M, Wada T, Fujiwara H, Sumi K (2003) Background subtraction based on cooccurrence of image variations. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 2. IEEE, pp II–II.Google Scholar
  46. 46.
    Sheikh Y, Shah M (2005) Bayesian modeling of dynamic scenes for object detection. IEEE Trans Pattern Anal Mach Intell 27(11):1778–1792CrossRefGoogle Scholar
  47. 47.
    Sobral A, Vacavant A (2014) A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput Vis Image Underst 122:4–21CrossRefGoogle Scholar
  48. 48.
    Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 2. pp 246–252Google Scholar
  49. 49.
    Tavakkoli A, Ambardekar A, Nicolescu M, Louis S (2007) A genetic approach to training support vector data descriptors for background modeling in video data. In: International Symposium on Visual Computing, pp 318–327Google Scholar
  50. 50.
    Tezuka H, Nishitani T (2008) A precise and stable foreground segmentation using fine-to-coarse approach in transform domain. In: 15Th IEEE International Conference on Image Processing, pp 2732–2735Google Scholar
  51. 51.
    Tian Y, Senior A, Lu M (2012) Robust and efficient foreground analysis in complex surveillance videos. Mach Vis Appl 23(5):967–983CrossRefGoogle Scholar
  52. 52.
    Tibshirani R (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), pp 267–288Google Scholar
  53. 53.
    Toyama K, Krumm J, Brumitt B, Meyers B (1999) Wallflower: Principles and practice of background maintenance. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol 1, pp 255–261Google Scholar
  54. 54.
    Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666MathSciNetCrossRefGoogle Scholar
  55. 55.
    Wren CR, Azarbayejani A, Darrell T, Pentland AP (1997) Pfinder: Real-time tracking of the human body. IEEE Trans Pattern Anal Mach Intell 19(7):780–785CrossRefGoogle Scholar
  56. 56.
    Wren CR, Porikli F (2005) Waviz: Spectral similarity for object detection. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp 55–61Google Scholar
  57. 57.
    Xiao M, Han C, Kang X (2006) A background reconstruction for dynamic scenes. In: 9th International Conference on Information Fusion, pp 1–7Google Scholar
  58. 58.
    Xu Y, Dong J, Zhang B, Xu D (2016) Background modeling methods in video analysis: a review and comparative evaluation. CAAI Transactions on Intelligence Technology 1(1):43–60CrossRefGoogle Scholar
  59. 59.
    Yang L, Li J, Luo Y, Zhao Y, Cheng H, Li J (2018) Deep background modeling using fully convolutional network. IEEE Trans Intell Transp Syst 19(1):254–262CrossRefGoogle Scholar
  60. 60.
    Zhang R, Zhang S, Yu S (2007) Moving objects detection method based on brightness distortion and chromaticity distortion. IEEE Transactions on Consumer Electronics 53(3)Google Scholar
  61. 61.
    Zhang S, Yao H, Liu S, Chen X, Gao W (2008) A covariance-based method for dynamic background subtraction. In: 19Th International Conference on Pattern Recognition, pp 1–4Google Scholar
  62. 62.
    Zheng J, Chakraborty J, Chapman WC, Gerst S, Gonen M, Pak LM, Jarnagin WR, DeMatteo RP, Do RK, Simpson AL (2018) Preoperative prediction of microvascular invasion in hepatocellular carcinoma using quantitative image analysis. J Am Coll Surg 225(6):778–788.e1CrossRefGoogle Scholar
  63. 63.
    Zheng J, Wang Y, Nihan N, Hallenbeck M (2006) Extracting roadway background image: mode-based approach. Transportation Research Record: Journal of the Transportation Research Board (1944), 82–88Google Scholar
  64. 64.
    Zivkovic Z (2004) Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, vol 2. pp 28–31Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Instrumentation EngineeringNational Institute of Technology SilcharSilcharIndia
  2. 2.Department of SurgeryMemorial Sloan Kettering Cancer CenterNew YorkUSA

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