References

• 1. 1)
     • 26. Seo, J.-W., Kim, S.D.: ‘Recursive on-line (2D)2PCA and its application to long-term background subtraction’, IEEE Trans. Multimed., 2014, 16, pp. 2333–2344.
  2. 2)
     • 6. Pal, A., Schaefer, G., Celebi, M.E.: ‘Robust codebook-based video background subtraction’. 2010 IEEE Int. Conf. on Acoustics Speech and Signal Processing, Dallas, Texas, U.S.A., 2010, pp. 1146–1149.
  3. 3)
     • 48. Cevikalp, H., Neamtu, M., Wilkes, M.: ‘Discriminative common vector method with kernels’, IEEE Trans. Neural Netw., 2006, 17, pp. 1550–1565.
  4. 4)
     • 22. Marghes, C., Bouwmans, T., Vasiu, R.: ‘Background modeling and foreground detection via a reconstructive and discriminative subspace learning approach’. Proc. Int. Conf. on Image Processing, Computer Vision, and Pattern Recognition (IPCV), Las Vegas, Nevada, U.S.A., 2012, p. 1.
  5. 5)
     • 14. Wang, R., Bunyak, F., Seetharaman, G., et al: ‘Static and moving object detection using flux tensor with split Gaussian models’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition Workshops, Columbus, Ohio, U.S.A., 2014, pp. 414–418.
  6. 6)
     • 39. Gulmezoglu, M., Dzhafarov, V., Barkana, A.: ‘The common vector approach and its relation to principal component analysis’, IEEE Trans. Speech Audio Process., 2001, 9, pp. 655–662.
  7. 7)
     • 17. Bucak, S.S., Günsel, B., Gursoy, O.: ‘Incremental non-negative matrix factorization for dynamic background modelling’. IEEE 15th Signal Processing and Communications Applications, Eskisehir, Turkey, 2007, pp. 107–116.
  8. 8)
     • 1. Panahi, S., Sheikhi, S., Hadadan, S., et al: ‘Evaluation of background subtraction methods’. Digital Image Computing: Techniques and Applications (DICTA), Canberra, Australia, 2008, pp. 357–364.
  9. 9)
     • 20. Bouwmans, T.: ‘Subspace learning for background modeling: a survey’, Recent Patents Comput. Sci., 2009, 2, pp. 223–234.
  10. 10)
      • 21. Oliver, N.M., Rosario, B., Pentland, A.P.: ‘A Bayesian computer vision system for modeling human interactions’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, pp. 831–843.
  11. 11)
      • 23. Farcas, D., Marghes, C., Bouwmans, T.J.M.V.: ‘Applications, background subtraction via incremental maximum margin criterion: a discriminative subspace approach’, Mach. Vis. Appl., 2012, 23, pp. 1083–1101.
  12. 12)
      • 46. Hofmann, M., Tiefenbacher, P., Rigoll, G.: ‘Background segmentation with feedback: the pixel-based adaptive segmenter’. 2012 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), Providence, Rhode Island, USA, 2012, pp. 38–43.
  13. 13)
      • 24. Yan, J., Zhang, B., Yan, S., et al: ‘IMMC: incremental maximum margin criterion’. Proc. Tenth ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, ACM, Seattle, Washington, U.S.A., 2004, pp. 725–730.
  14. 14)
      • 31. Rodríguez, P., Wohlberg, B.: ‘Translational and rotational jitter invariant incremental principal component pursuit for video background modeling’. IEEE Int. Conf. on Image Processing (ICIP), Quebec City, Quebec, Canada, 2015, pp. 537–541.
  15. 15)
      • 2. Sobral, A., Vacavant, A.: ‘A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos’, Comput. Vis. Image Underst., 2014, 122, pp. 4–21.
  16. 16)
      • 4. Bouwmans, T.: ‘Traditional and recent approaches in background modeling for foreground detection: an overview’, J. Comput. Sci. Rev., 2014, 11, pp. 31–66.
  17. 17)
      • 5. Haritaoglu, I., Harwood, D., Davis, L.S.: ‘W4: real-time surveillance of people and their activities’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, pp. 809–830.
  18. 18)
      • 29. Zhou, T., Tao, D.: ‘Godec: randomized low-rank & sparse matrix decomposition in noisy case’. Proc. 28th Int. Conf. on Machine Learning, ICML, Bellevue, Washington, U.S.A., 2011.
  19. 19)
      • 44. Işık, Ş., Özkan, K., Günal, S.Ö., et al: ‘SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos’, J. Electron. Imaging, 2018, 27, p. 023002.
  20. 20)
      • 36. Wang, Y., Luo, Z., Jodoin, P.-M.: ‘Interactive deep learning method for segmenting moving objects’, Pattern Recognit. Lett., 2017, 96, pp. 66–75.
  21. 21)
      • 38. Lim, L.A., Keles, H.Y.: ‘Foreground segmentation using convolutional neural networks for multiscale feature encoding’, Pattern Recognit. Lett., 2018, 112, pp. 256–262.
  22. 22)
      • 28. Qiu, C., Vaswani, N.: ‘Real-time robust principal components’ pursuit’. 2010 48th Annual Allerton Conf. on Communication, Control, and Computing (Allerton) IEEE, Allerton, Illinois, U.S.A., 2010, pp. 591–598.
  23. 23)
      • 8. Wren, C.R., Azarbayejani, A., Darrell, T., et al: ‘Pfinder: real-time tracking of the human body’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, pp. 780–785.
  24. 24)
      • 41. Günal, S., Ergin, S., Gerek, Ö.N.: ‘Spam E-mail recognition by subspace analysis’. INISTA–Int. Symp. on Innovations in Intelligent Systems and Applications, Istanbul, Türkiye, 2005, pp. 307–310.
  25. 25)
      • 34. Bouwmans, T., Sobral, A., Javed, S., et al: ‘Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset’, Comput. Sci. Rev., 2017, 23, pp. 1–71.
  26. 26)
      • 7. Piccardi, M.: ‘Background subtraction techniques: a review’. 2014 IEEE Int. Conf. on Systems, Man and Cybernetics, 2004, pp. 3099–3104.
  27. 27)
      • 33. Javed, S., Jung, S.K., Mahmood, A., et al: ‘Motion-aware graph regularized RPCA for background modeling of complex scenes’. 2016 23rd Int. Conf. on Pattern Recognition (ICPR), Cancun, Mexico, 2016, pp. 120–125.
  28. 28)
      • 10. Chen, Y., Wang, J., Lu, H.: ‘Learning sharable models for robust background subtraction’. 2015 IEEE Int. Conf. on Multimedia and Expo (ICME), Turin, Italy, 2015, pp. 1–6.
  29. 29)
      • 30. Babacan, S.D., Luessi, M., Molina, R., et al: ‘Sparse Bayesian methods for low-rank matrix estimation’, IEEE Trans. Signal Process., 2012, 60, pp. 3964–3977.
  30. 30)
      • 32. Javed, S., Bouwmans, T., Jung, S.K.: ‘Combining ARF and OR-PCA for robust background subtraction of noisy videos’. Int. Conf. on Image Analysis and Processing, Genoa, Italy, 2015, pp. 340–351.
  31. 31)
      • 25. Hughes, K., Grzeda, V., Greenspan, M.: ‘Eigenbackground bootstrapping’. Int. Conf. on Computer and Robot Vision, IEEE, Regina, Saskatchewan, Canada, 2013, pp. 196–201.
  32. 32)
      • 43. Gülmezoğlu, M.B., Dzhafarov, V., Edizkan, R., et al: ‘The common vector approach and its comparison with other subspace methods in case of sufficient data’, Comput. Speech Lang., 2007, 21, pp. 266–281.
  33. 33)
      • 18. Li, X., Hu, W., Zhang, Z., et al: ‘Robust foreground segmentation based on two effective background models’. Proc. 1st ACM Int. Conf. on Multimedia information Retrieval, ACM, Vancouver, British Columbia, Canada, 2008, pp. 223–228.
  34. 34)
      • 13. St-Charles, P.-L., Bilodeau, G.-A., Bergevin, R.: ‘A self-adjusting approach to change detection based on background word consensus’. IEEE Winter Conf. on Applications of Computer Vision (WACV) IEEE, Waikoloa, Hawaii, U.S.A., 2015, pp. 990–997.
  35. 35)
      • 37. Babaee, M., Dinh, D.T., Rigoll, G.: ‘A deep convolutional neural network for video sequence background subtraction’, Pattern Recognit., 2018, 76, pp. 635–649.
  36. 36)
      • 11. Jiang, S., Lu, X.: ‘WeSamBE: a weight-sample-based method for background subtraction’, IEEE Trans. Circuits Syst. Video Technol., 2018, 28, pp. 2105–2115.
  37. 37)
      • 19. Stauffer, C., Grimson, W.E.L.: ‘Adaptive background mixture models for real-time tracking’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Fort Collins, Colorado, U.S.A., 1999.
  38. 38)
      • 42. Özkan, K, Işık, Ş.: ‘A novel multi-scale and multi-expert edge detector based on common vector approach’, AEU-Int. J. Electron. Commun., 2015, 69, pp. 1272–1281.
  39. 39)
      • 3. Cristani, M., Farenzena, M., Bloisi, D., et al: ‘Background subtraction for automated multisensor surveillance: a comprehensive review’, EURASIP J. Adv. Signal Process., 2010, 2010, p. 343057.
  40. 40)
      • 45. Agrawal, A., Raskar, R., Chellappa, R.: ‘Edge suppression by gradient field transformation using cross-projection tensors’. 2006 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition IEEE, New York, New York, USA, 2006, pp. 2301–2308.
  41. 41)
      • 15. Sajid, H., Cheung, S.-C.S.: ‘Universal multimode background subtraction’, IEEE Trans. Image Process., 2017, 26, pp. 3249–3260.
  42. 42)
      • 35. Vaswani, N., Bouwmans, T., Javed, S., et al: ‘Robust subspace learning: robust PCA, robust subspace tracking, and robust subspace recovery’, IEEE Signal Process. Mag., 2018, 35, pp. 32–55.
  43. 43)
      • 47. Yi, X., Ling, N.: ‘Fast pixel-based video scene change detection’. IEEE Int. Symp. on Circuits and Systems, 2005. ISCAS 2005 IEEE, Kobe, Japan, 2005, pp. 3443–3446.
  44. 44)
      • 40. Cevikalp, H., Neamtu, M., Wilkes, M., et al: ‘Discriminative common vectors for face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, pp. 4–13.
  45. 45)
      • 9. Wang, Y., Jodoin, P.-M., Porikli, F., et al: ‘CDnet 2014: an expanded change detection benchmark dataset’. 2014 IEEE Conf. on Computer Vision and Pattern Recognition Workshops, Columbus, Ohio, U.S.A., 2014, pp. 387–394.
  46. 46)
      • 27. De la Torre, F., Black, M.J.: ‘Robust principal component analysis for computer vision’. Int. Conf. on Computer Vision (ICCV) IEEE, Vancouver, British Columbia, Canada, 2001, pp. 362–369.
  47. 47)
      • 16. Tsai, D.-M., Lai, S.-C.: ‘Independent component analysis-based background subtraction for indoor surveillance’, IEEE Trans. Image Process., 2009, 18, pp. 158–167.
  48. 48)
      • 12. St-Charles, P.-L., Bilodeau, G.-A., Bergevin, R.: ‘Subsense: a universal change detection method with local adaptive sensitivity’, IEEE Trans. Image Process., 2015, 24, pp. 359–373.