Improved foreground detection via block-based classifier cascade with probabilistic decision integration

IEEE Transactions on Circuits and Systems for Video Technology

Volumn 23, Issue 1, 2013, Pages 83-93

  Reddy, Vikas ^a,b,c   Sanderson, Conrad ^a,b   Lovell, Brian C ^a,b  

^a CSIRO   (Australia)

^b UNIVERSITY OF QUEENSLAND   (Australia)

^c QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

Background modeling; background subtraction; cascade; foreground detection; patch analysis; segmentation

Indexed keywords

ADAPTIVE CLASSIFIERS; BACKGROUND MODELING; BACKGROUND SUBTRACTION; COMPUTER VISION APPLICATIONS; CONTEXTUAL INFORMATION; DATA SETS; DESCRIPTORS; DYNAMIC BACKGROUND; FOREGROUND DETECTION; FOREGROUND OBJECTS; FOREGROUND SEGMENTATION; ILLUMINATION VARIATION; IMAGE SEQUENCE; PATCH ANALYSIS; PIXEL-BASED METHODS; PROBABILISTIC DECISIONS; TRACKING ACCURACY; TRACKING PERFORMANCE;

CASCADES (FLUID MECHANICS); COMPUTER VISION; IMAGE SEGMENTATION;

PIXELS;

EID: 84872279270     PISSN: 10518215     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSVT.2012.2203199     Document Type: Article

References (59)

1. F. Porikli and O. Tuzel, "Human body tracking by adaptive background models and mean-shift analysis," in Proc. IEEE Int. Workshop Performance Eval. Tracking Surveillance, 2003.
2. O. Javed and M. Shah, "Tracking and object classification for automated surveillance," in Lecture Notes in Computer Science, vol. 2353. 2006, pp. 439-443.
3. F. Lv and R. Nevatia, "Single view human action recognition using key pose matching and Viterbi path searching," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2007, pp. 1-8.
4. L. Wang, T. Tan, H. Ning, and W. Hu, "Silhouette analysis-based gait recognition for human identification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 12, pp. 1505-1518, Dec. 2003.
5. T. Xiang and S. Gong, "Incremental and adaptive abnormal behavior detection," Comput. Vis. Image Understanding, vol. 111, no. 1, pp. 59-73, 2008.
6. V. Reddy, C. Sanderson, and B. Lovell, "Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture," in Proc. IEEE Conf. Comput. Vision Pattern Recognit. Workshops, 2011, pp. 55-61.
7. A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov, "Bilayer segmentation of live video," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., vol. 1. 2006, pp. 53-60. (Pubitemid 44931312)
8. J. Vass, K. Palaniappan, and X. Zhuang, "Automatic spatio-temporal video sequence segmentation," in Proc. IEEE Int. Conf. Image Process., vol. 1. Oct. 1998, pp. 958-962.
9. A. Neri, S. Colonnese, G. Russo, and P. Talone, "Automatic moving object and background separation," Signal Process., vol. 66, no. 2, pp. 219-232, 1998. (Pubitemid 128420636)
10. A. Agarwala, M. Dontcheva, M. Agrawala, S. Drucker, A. Colburn, B. Curless, D. Salesin, and M. Cohen, "Interactive digital photomontage," ACM Trans. Graph., vol. 23, pp. 294-302, 2004.
11. M. Piccardi, "Background subtraction techniques: A review," in Proc. IEEE Int. Conf. Syst. Man Cybern., vol. 4. Oct. 2004, pp. 3099-3104. (Pubitemid 40408308)
12. S. Brutzer, B. Ḧoferlin, and G. Heidemann, "Evaluation of background subtraction techniques for video surveillance," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., Jun. 2011, pp. 1937-1944.
13. T. Bouwmans, F. El Baf, and B. Vachon, "Background modeling using mixture of Gaussian for foreground detection: A survey," Recent Patents Comput. Sci., vol. 1, no. 3, pp. 219-237, 2008.
14. T. Bouwmans, F. El Baf, and B. Vachon, "Statistical background modeling for foreground detection: A survey," Handbook of Pattern Recognition and Computer Vision, vol. 4. Singapore: World Scientific, 2010, ch. 2.3, pp. 181-199.
15. J. Barron, D. Fleet, and S. Beauchemin, "Performance of optical flow techniques," Int. J. Comput. Vision, vol. 12, no. 1, pp. 43-77, 1994.
16. P. Kohli and P. Torr, "Dynamic graph cuts for efficient inference in Markov random fields," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 12, pp. 2079-2088, Dec. 2007. (Pubitemid 350124058)
17. P. Viola and M. Jones, " Robust real-time face detection," Int. J. Comput. Vision, vol. 57, no. 2, pp. 137-154, 2004.
18. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., vol. 1. Jun. 2005, pp. 886-893. (Pubitemid 43897286)
19. E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 1998.
20. V. Reddy, C. Sanderson, A. Sanin, and B. Lovell, "Adaptive patch-based background modeling for improved foreground object segmentation and tracking," in Proc. Int. Conf. Advanced Video Signal Based Surveillance, 2010, pp. 172-179.
21. M. Cristani, M. Farenzena, D. Bloisi, and V. Murino, "Background subtraction for automated multisensor surveillance: A comprehensive review," EURASIP J. Advances Signal Process., vol. 2010, pp. 1-24, 2010.
22. C. Stauffer and W. Grimson, "Adaptive background mixture models for real-time tracking," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., vol. 2. 1999, pp. 246-252.
23. P. KaewTraKulPong and R. Bowden, "An improved adaptive background mixture model for real-time tracking with shadow detection," in Proc. 2nd Eur. Workshop Advanced Video Based Surveillance Syst., vol. 1. 2001, pp. 1-5.
24. Z. Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction," in Proc. Int. Conf. Pattern Recognit., vol. 2. 2004, pp. 28-31.
25. L. Teixeira, J. Cardoso, and L. Corte-Real, "Object segmentation using background modeling and cascaded change detection," J. Multimedia, vol. 2, no. 5, pp. 55-65, 2007.
26. A. Elgammal, D. Harwood, and L. Davis, "Non-parametric model for background subtraction," in Lecture Notes in Computer Science, vol. 1843. 2000, pp. 751-767.
27. L. Li, W. Huang, I. Gu, and Q. Tian, "Foreground object detection from videos containing complex background," in Proc. ACM Int. Conf. Multimedia, 2003, pp. 2-10.
28. B. Han and L. Davis, "Density-based multifeature background subtraction with support vector machine," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 5, pp. 1017-1023, May 2012.
29. T. Parag, A. Elgammal, and A. Mittal, "A framework for feature selection for background subtraction," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., vol. 2. 2006, pp. 1916-1923. (Pubitemid 44931550)
30. L. Cheng, M. Gong, D. Schuurmans, and T. Caelli, "Real-time discriminative background subtraction," IEEE Trans. Image Process., vol. 20, no. 5, pp. 1401-1414, May 2011.
31. A. Shimada and R. Taniguchi, "Hybrid background model using spatialtemporal LBP," in Proc. IEEE Int. Conf. Advanced Video Signal Based Surveillance, 2009, pp. 19-24.
32. T. Tanaka, A. Shimada, D. Arita, and R.-I. Taniguchi, "Object detection under varying illumination based on adaptive background modeling considering spatial locality," in Lecture Notes in Computer Science, vol. 5414. 2009, pp. 645-656.
33. J.-M. Guo, Y.-F. Liu, C.-H. Hsia, M.-H. Shih, and C.-S. Hsu, "Hierarchical method for foreground detection using codebook model," IEEE Trans. Circuits Syst. Video Technol., vol. 21, no. 6, pp. 804-815, Jun. 2011.
34. O. Javed and M. Shah, Automated Multi-Camera Surveillance: Algorithms and Practice. Berlin, Germany: Springer, 2008.
35. N. Oliver, B. Rosario, and A. Pentland, "A Bayesian computer vision system for modeling human interactions," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 831-843, Aug. 2000.
36. L. Wang, L. Wang, M. Wen, Q. Zhuo, and W. Wang, "Background subtraction using incremental subspace learning," in Proc. IEEE Int. Conf. Image Process., vol. 5. Sep.-Oct. 2007, pp. 45-48.
37. E. Ĺopez-Rubio and R. M. L. Baena, "Stochastic approximation for background modeling," Comput. Vision Image Understanding, vol. 115, no. 6, pp. 735-749, 2011.
38. L. Maddalena and A. Petrosino, "A self-organizing approach to background subtraction for visual surveillance applications," IEEE Trans. Image Process., vol. 17, no. 7, pp. 1168-1177, Jul. 2008.
39. E. Ĺopez-Rubio, R. M. L. Baena, and E. Doḿ?nguez, "Foreground detection in video sequences with probabilistic self-organizing maps," Int. J. Neural Syst., vol. 21, no. 3, pp. 225-246, 2011.
40. R. Gonzalez and R. Woods, Digital Image Processing, 3rd ed. Englewood Cliffs, NJ: Pearson Prentice-Hall, 2007.
41. M. Mason and Z. Duric, "Using histograms to detect and track objects in color video," in Proc. Appl. Imagery Pattern Recognit. Workshop, 2001, pp. 154-159.
42. T. Matsuyama, T. Wada, H. Habe, and K. Tanahashi, "Background subtraction under varying illumination," Syst. Comput. Japan, vol. 37, no. 4, pp. 77-88, 2006.
43. H. Grabner, C. Leistner, and H. Bischof, "Time dependent on-line boosting for robust background modeling," in Proc. Comput. Vision Theory Applicat., vol. 1. 2008, pp. 612-618.
44. Y. Lee, J. Jung, and I. Kweon, "Hierarchical on-line boosting based background subtraction," in Proc. Workshop Frontiers Comput. Vision, 2011, pp. 1-5.
45. M. Seki, T. Wada, H. Fujiwara, and K. Sumi, "Background subtraction based on co-occurrence of image variations," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., vol. 2. 2003, pp. 65-72.
46. A. Chan and N. Vasconcelos, "Modeling clustering, and segmenting video with mixtures of dynamic textures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 5, pp. 909-926, May 2008. (Pubitemid 351486757)
47. D. Pokrajac and L. Latecki, "Spatiotemporal blocks-based moving objects identification and tracking," in Proc. IEEE Visual Surveillance Performance Evaluation Tracking Surveillance, 2003, pp. 70-77.
48. A. Sanin, C. Sanderson, and B. Lovell, "Shadow detection: A survey and comparative evaluation of recent methods," Pattern Recognit., vol. 45, no. 4, pp. 1684-1695, 2012.
49. C. Sanderson and B. Lovell, "Multi-region probabilistic histograms for robust and scalable identity inference," in Lecture Notes in Computer Science, vol. 5558. 2009, pp. 199-208.
50. D. Baltieri, R. Vezzani, and R. Cucchiara, "Fast background initialization with recursive Hadamard transform," in Proc. Int. Conf. Advanced Video Signal Based Surveillance, 2010, pp. 165-171.
51. V. Reddy, C. Sanderson, and B. Lovell, "A low complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts," EURASIP J. Image Video Process., vol. 2011, pp. 1-14, 2011.
52. R. Duda, P. Hart, and D. Stork, Pattern Classification, 2nd ed. New York: Wiley, 2001.
53. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, "Pfinder: Realtime tracking of the human body," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 780-785, Jul. 1997. (Pubitemid 127762271)
54. K. Kim, T. Chalidabhongse, D. Harwood, and L. Davis, "Real-time foreground-background segmentation using codebook model," Real-Time Imaging, vol. 11, no. 3, pp. 172-185, 2005. (Pubitemid 40929458)
55. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, "Wallflower: Principles and practice of background maintenance," in Proc. IEEE Int. Conf. Comput. Vision, vol. 1. 1999, pp. 255-261. (Pubitemid 32080048)
56. C. Sanderson, "Armadillo: An open source C++ linear algebra library for fast prototyping and computationally intensive experiments," NICTA, St. Lucia, Australia, Tech. Rep., 2010.
57. G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision With the OpenCV Library. Sebastopol, CA: O'Reilly Media, 2008.
58. K. Bernardin and R. Stiefelhagen, "Evaluating multiple object tracking performance: The CLEAR MOT metrics," EURASIP J. Image Video Process., vol. 2008, pp. 1-10, 2008.
59. J. Munkres, "Algorithms for the assignment and transportation problems," J. Soc. Ind. Appl. Math., vol. 5, no. 1, pp. 32-38, 1957.