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Spatiotemporal optical blob reconstruction for object detection in grayscale videos

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Abstract

There has been a significant research devoted towards detection of a moving object in an image sequence. Detected moving objects usually contain some errors (some pixels belonging to the object are marked as non-objects and vice versa). To achieve a refined detection of moving object in the video, there is a need of post processing of the binary blobs detected as objects in every frame of the video. This article introduces a novel blob reconstruction method that overcomes the mentioned limitation through optical flow based nullification, bifurcation, and unification of detected blobs. To claim the performance of the proposed method, a comparison is made with ten widely used object detection methods on twenty four standard moving-object scene videos. Comparison is made based on standard parameters like accuracy, precision, recall, and F-measure. The results clearly indicates the efficacy of the proposed method. Following this, results on a priliminary case study on placodal cell migration during early development of ectodermal organ of human and mice has been made employing the proposed model which promisingly tracks the cell migration.

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References

  1. Barnich O, Van Droogenbroeck M (2011) ViBe: A universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724. doi:10.1109/TIP.2010.2101613

    Article  MathSciNet  MATH  Google Scholar 

  2. Correia P, Pereira F (2000) Objective evaluation of relative segmentation quality. In: International Conference on Image Processing, pp 308–311. doi:10.1109/ICIP.2000.900956

  3. El Baf F, Bouwmans T, Vachon B (2008) Fuzzy integral for moving object detection. In: International Conference on Fuzzy Systems, pp 1729–1736. doi:10.1109/FUZZY.2008.4630604

  4. Erdem CE, Sankur B, Tekalp AM (2004) Performance measures for video object segmentation and tracking. IEEE Trans Image Process 13(7):937–951. doi:10.1109/TIP.2004.828427

    Article  Google Scholar 

  5. Goyette N, Jodoin P-M, Porikli F, Konrad J, Ishwar P (2012) Changedetection. net: A new change detection benchmark dataset. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp 1–8. doi:10.1109/CVPRW.2012.6238919

  6. Haritaoglu I, Harwood D, Davis LS (1998) W 4: Who? When? Where? What? A real time system for detecting and tracking people. IEEE International Conference on Automatic Face and Gesture Recognition:222–227. doi:10.1109/AFGR.1998.670952

  7. Horn BKP, Schunck BG (1981) Determining optical flow. Artificial Intelligence 17(1-3):185203. doi:10.1016/0004-3702(81)90024-2

    Article  Google Scholar 

  8. Hofmann M, Tiefenbacher P, Rigoll G (2012) Background segmentation with feedback: The pixel-based adaptive segmenter. In: Computer Vision and Pattern Recognition Workshops, pp 38–43. doi:10.1109/CVPRW.2012.6238925

  9. Jacques JCS, Jung CR, Raupp Musse S (2005) Background subtraction and shadow detection in grayscale video sequences. In: 18th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI), pp 189–196. doi:10.1109/SIBGRAPI.2005.15

  10. Jung CR (2009) Efficient background subtraction and shadow removal for monochromatic video sequences. IEEE Trans Multimedia 11(3):571–577. doi:10.1109/TMM.2009.2012924

    Article  Google Scholar 

  11. KaewTraKulPong P, Bowden R (2002) An improved adaptive background mixture model for real-time tracking with shadow detection. In: Video-based surveillance systems, pp 135–144. doi:10.1007/978-1-4615-0913-4_11

  12. Kim W, Kim C (2011) Background subtraction for dynamic texture scenes using Fuzzy color histograms. IEEE Signal Process Lett 19(3):127–130. doi:10.1109/LSP.2011.2182648

    Article  Google Scholar 

  13. Koller D, Weber J, Huang T, Malik J, Ogasawara G, Rao B, Russel S (1994) Towards robust automatic traffic scene analysis in real-time. Int Conf Pattern Recog (ICPR):126–131. doi:10.1109/CDC.1994.411746

  14. Li L, Huang W, Gu IYH, Tian Q (2003) Foreground object detection from videos containing complex background. International conference on Multimedia:2–10. doi:10.1145/957013.957017

  15. Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: 7th International Joint Conference on Artificial Intelligence, pp 674–679

  16. Maddalena L, Petrosino A (2008) A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans Image Process 17(7):1168–1177. doi:10.1109/TIP.2008.924285

    Article  MathSciNet  Google Scholar 

  17. McKenna SJ, Raja Y, Gong S (1999) Tracking colour objects using adaptive mixture models. Image and Vision Computing 17(3-4):225–231. doi:10.1016/S0262-8856(98)00104-8

    Article  Google Scholar 

  18. Nair D, Aggarwal JK (1998) Moving obstacle detection from a navigating robot. IEEE Trans Robot Autom 14(3):404–416. doi:10.1109/70.678450

    Article  Google Scholar 

  19. Reddy V, Sanderson C, Lovell BC (2013) Improved foreground detection via block-based classifier cascade with probabilistic decision integration. IEEE Trans Circuits Syst Video Technol 23(1):83–93. doi:10.1109/TCSVT.2012.2203199

    Article  Google Scholar 

  20. Rodriguez P, Wohlberg B (2013) Fast principal component pursuit via alternating minimization. In: IEEE International Conference on Image Processing, pp 69–73. doi:10.1109/ICIP.2013.6738015

  21. Seki M, Fujiwara H, Sumi K (2000) A robust background subtraction method for changing background. IEEE Workshop on Applications of Computer Vision:207–213. doi:10.1109/WACV.2000.895424

  22. Stauffer C, Eric W, Grimson L (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757. doi:10.1109/34.868677

    Article  Google Scholar 

  23. Thompson WB, Mutch KM, Berzins VA (1985) Dynamic occlusion analysis in optical flow fields. IEEE Trans Pattern Anal Mach Intell 7(4):374–383. doi:10.1109/TPAMI.1985.4767677

    Article  Google Scholar 

  24. Toyama K, Krumm J, Brumitt B, Meyers B (1999) Wallflower: Principles and practice of background maintenance. International Conference on Computer Vision 1:255–261. doi:10.1109/ICCV.1999.791228

    Google Scholar 

  25. Wang Y, Jodoin P-M, Porikli F, Konrad J, Benezeth Y, Ishwar P (2014) CDnet 2014: an expanded change detection benchmark dataset. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 387–394. doi:10.1109/CVPRW.2014.126

  26. Weickert J, Schnorr C (2001) Variational optic flow computation with a spatio-temporal smoothness constraint. J Math Imaging Vision 14(3):245–255. doi:10.1023/A:1011286029287

    Article  MATH  Google Scholar 

  27. 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):780–785. doi:10.1109/34.598236

    Article  Google Scholar 

  28. Yao J, Odobez J-M (2007) Multi-layer background subtraction based on color and texture. In: Computer Vision and Pattern Recognition, pp 1–8. doi:10.1109/CVPR.2007.383497

  29. Yao Q, Liu Q, Dietterich TG, Todorovic S, Lin J, Diao G, Yang B, Tang J (2013) Segmentation of touching insects based on optical flow and NCuts. Biosyst Eng 114(2):67–77. doi:10.1016/j.biosystemseng.2012.11.008

    Article  Google Scholar 

  30. Zhou T, Tao D (2011) Godec: Randomized low-rank andamp; sparse matrix decomposition in noisy case, International Conference on Machine Learning

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Acknowledgments

The work presented in this article is supported by Grant No. ETI/359/2014 by Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (FIST) Program 2016, Department of Science and Technology, Government of India.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Centre for Computer Vision and Pattern Recognition, National Institute of Technology Rourkela, Odisha, 769 008, India

    Rahul Raman, Suman Kumar Choudhury & Sambit Bakshi

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  1. Rahul Raman
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  2. Suman Kumar Choudhury
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  3. Sambit Bakshi
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Correspondence to Sambit Bakshi.

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Raman, R., Choudhury, S.K. & Bakshi, S. Spatiotemporal optical blob reconstruction for object detection in grayscale videos. Multimed Tools Appl 77, 741–762 (2018). https://doi.org/10.1007/s11042-016-4234-0

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