Articulated human body parts detection based on cluster background subtraction and foreground matching

Neurocomputing

Volumn 100, Issue , 2013, Pages 58-73

  Bhaskar, Harish ^a   Mihaylova, Lyudmila ^b   Maskell, Simon ^c  

^a KHALIFA UNIVERSITY   (United Arab Emirates)

^b LANCASTER UNIVERSITY   (United Kingdom)

^c QINETIQ   (United Kingdom)

Author keywords

Background subtraction; Genetic algorithm; Human target tracking; Optimisation; Pictorial structures

Indexed keywords

ARTICULATED OBJECT; BACKGROUND SUBTRACTION; BODY PARTS; COMPUTER VISION PROBLEMS; GAUSSIAN MIXTURE MODEL; HUMAN BODIES; OPTIMISATIONS; PICTORIAL STRUCTURES; SELF-ADAPTIVE; TRACKING IN VIDEO SEQUENCES; VIDEO SEQUENCES;

COMPUTER VISION; GENETIC ALGORITHMS; TARGET TRACKING;

STRUCTURAL OPTIMIZATION;

ACCURACY; ANALYTICAL PARAMETERS; ARTICLE; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; CLUSTER BACKGROUND SUBTRACTION MODEL; COMPARATIVE EFFECTIVENESS; COMPUTER MODEL; CONTROLLED STUDY; GAUSSIAN MIXTURE MODEL; HUMAN; HUMAN BODY PART TRACKING; IMAGE ANALYSIS; INFORMATION PROCESSING; KERNEL METHOD; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; ROBUSTNESS; STATISTICAL ANALYSIS; STATISTICAL MODEL;

EID: 84868612418     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.12.039     Document Type: Article

References (48)

1. C. Stauffer, W. Grimson, Adaptive background mixture models for real-time tracking, in: Proceedings of IEEE Computer Vision and Pattern Recognition Conference, 1999, pp. 246-252.
2. Stauffer C., Grimson W. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22(8):7747-7757.
3. A. McIvor, Background subtraction techniques, in: Proceedings of Image and Vision Computing, Auckland, New Zealand, 2000.
4. Cucchiara R., Grana C., Piccardi M., Prati A. Detecting moving objects, ghosts and shadows in video streams. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 25(10):1337-1342.
5. Foresti G., Micheloni C., Snidaro L., Remagnino P., Ellis T. Active video-based surveillance system: the low-level image and video processing techniques needed for implementation. IEEE Signal Process. Mag. 2005, 22(March (2)):25-37.
6. B. Lo, S. Velastin, Automatic congestion detection system for underground platforms, in: Proceedings of the 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2001, pp. 158-161.
7. Zivkovic Z., van der Heijden F. Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Lett. 2006, 27(7):773-780.
8. Pavlidis I., Morellas V., Tsiamyrtzis P., Harp S. Urban surveillance systems: from the laboratory to the commercial world. Proc. IEEE 2001, 89(10):1478-1497.
9. A. Elgammal, D. Harwood, L. Davis, Non-parametric model for background subtraction, in: Proceedings of the 6th European Conference on Computer Vision, June/July 2000.
10. Elgammal A., Duraiswami R., Harwood D., Davis L.S. Background and foreground modeling using non-parametric kernel density estimation for visual surveillance. Proc. IEEE 2002, 90(July):1151-1163.
11. B. Han, D. Comaniciu, Y. Zhu, L. Davis, Incremental density approximation and kernel-based Bayesian filtering for object tracking, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2004.
12. P. Felzenswalb, Learning models for object recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2001.
13. Magee D.R. Tracking multiple vehicles using foreground, background and motion models, Image and Vision Computing. Statistical Methods in Video Processing 2004, 22(2):143-155.
14. Mitchell M. An Introduction to Genetic Algorithms (Complex Adaptive Systems) 1998, MIT Press.
15. Felzenszwalb P.F., Huttenlocher D.P. Pictorial structures for object recognition. Int. J. Comput. Vision 2005, 61(1):55-79.
16. P. Felzenswalb, D. Huttenlocher, Efficient matching of pictorial structures, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2000.
17. Forsyth D., Arikan O., Ikemoto L., Ramanan D. Computational Studies of Human Motion. Part 1. Tracking and Motion Synthesis, Foundations and Trends in Computer Graphics and Vision 2006, Now Publishers Inc., Hanover, MA.
18. Ramanan D., Forsyth D.A., Zisserman A. Tracking people by learning their appearance. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29(1):65-81.
19. Bergtholdt M., Kappes J., Schmidt S., Schnrr C. A study of parts-based object class detection using complete graphs. Int. J. Comput. Vision 2010, 87:93-117.
20. M. Andriluka, S. Roth, B. Schiele, Pictorial structures revisited: people detection and articulated pose estimation, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009, pp. 1014-1021.
21. M. Andriluka, S. Roth, B. Schiele, Monocular 3D pose estimation and tracking by detection, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 623-630.
22. Turk M., Pentland A. Eigenfaces for recognition. J. Cognitive Neurosci. 1991, 3(1):71-96.
23. Murase H., Nayar S. Visual learning and recognition of 3-D objects from appearance. Int. J. Comput. Vision 1995, 14(1):5-24.
24. Huttenlocher D., Klanderman G., Rucklidge W. Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 1993, 15(9):850-863.
25. A. Pentland, Recognition by parts, in: IEEE International Conference on Computer Vision, 1987, pp. 612-620.
26. S. Dickinson, I. Biederman, A. Pentland, J. Eklundh, R. Bergevin, R. Munck-Fairwood, The use of geons for generic 3-D object recognition, in: International Joint Conference on Artificial Intelligence, 1993, pp. 1693-1699.
27. Rivlin E., Dickinson S., Rosenfeld A. Recognition by functional parts. Comput. Vision Image Understanding 1995, 62(2):164-176.
28. M. Burl, P. Perona, Recognition of planar object classes, in:IEEE Conference on Computer Vision and Pattern Recognition, 1996, pp. 223-230.
29. Ioffe S., Forsyth D. Probabilistic methods for finding people. Int. J. Comput. Vision 2001, 43(1):45-68.
30. Gdalyahu Y., Weinshall D. Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes. IEEE Trans. Pattern Anal. Mach. Intell. 1999, 21(12):1312-1328.
31. T. Sebastian, P. Klein, B. Kimia, Recognition of shapes by editing shock graphs, in: IEEE International Conference on Computer Vision 2001, pp. 755-762.
32. C. Bregler, J. Malik, Tracking people with twists and exponential maps, in: IEEE Conference on Computer Vision and Pattern Recognition, 1998, pp. 8-15.
33. S. Ju, M. Black, Y. Yacoob, Cardboard people: a parameterized model of articulated motion, in: International Conference on Automatic Face and Gesture Recognition, 1996, pp. 38-44.
34. M.W. Lee, I. Cohen, Proposal maps driven MCMC for estimating human body pose in static images, in: Proceedings of IEEE Computer Vision and Pattern Recognition Conference, 2004.
35. Peursum P., Venkatesh S., West G. A study on smoothing for particle-filtered 3D human body tracking. Int. J. Comput. Vision 2010, 87:53-74.
36. F. DiMaio, J.W. Shavlik, G.N. Phillips, Pictorial structures for molecular modeling: interpreting density maps, in: NIPS, 2004.
37. M. Kumar, P.H. S. Torr, A. Zisserman, Extending pictorial structures for object recognition, in: Proceedings of the British Machine Vision Conference, 2004, pp. 789-798.
38. H. Hirschmuller, D. Scharstein, Evaluation of cost functions for stereo matching, in: CVPR. IEEE Computer Society.
39. N. Dekker, L.S. Ploeger, M. van Herk, Evaluation of cost functions for gray value matching of 2D images in radiotherapy, in: Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer-Verlag, London, UK, 2001, pp. 1354-1357.
40. van der Heijden F., Duin R., de Ridder D. Classification, Parameter Estimation and State Estimation 2004, John Wiley and Sons.
41. A.M. Payne, H. Bhaskar, L. Mihaylova, Multi-resolution learning vector quantisation based automatic colour clustering, in: FUSION Conference, Cologne, Germany, 2008.
42. Maskell S. A Bayesian approach to fusing uncertain, imprecise and conflicting information. Inf. Fusion 2008, 9(2):259-277.
43. R. Gross, J. Shi, The CMU Motion of Body (MoBo) Database (CMU-RI-TR-01-18), Technical Report, Robotics Inst., Carnegie Mellon Univ., 2001. The data are available at: http://mocap.cs.cmu.edu/.
44. Caviar Test Case Scenarios, 〈〉, 2005. http://homepages.inf.ed.ac.uk/rbf/caviardata1/.
45. Bhaskar H., Mihaylova L., Achim A. Video foreground detection based on symmetric alpha-stable mixture models. IEEE Trans. Circuits Syst. Video Technol. 2010, 20(August (8)):1133-1138.
46. J. Davis, M. Goadrich, The relationship between precision-recall and ROC curves, in: Proceedings of the 23rd International Conference on Machine Learning, 2006.
47. Cheung S.-C., Kamath C. Robust techniques for background subtraction in urban traffic video. Video Commun. Image Process. 2004, 5308(1):881-892.
48. B. Sapp, A. Toshev, B. Taskar, Cascaded models for articulated pose estimation, in: Proceedings of the European Conference on Computer Vision (ECCV), 2010.