Inter-occlusion reasoning for human detection based on variational mean field

Neurocomputing

Volumn 110, Issue , 2013, Pages 51-61

  Nguyen, Duc Thanh ^a   Li, Wanqing ^a   Ogunbona, Philip O ^a  

^a UNIVERSITY OF WOLLONGONG   (Australia)

Author keywords

Human detection; Mean field method; Non redundant local binary pattern; Occlusion reasoning

Indexed keywords

DETECTION PERFORMANCE; ESTIMATION TECHNIQUES; HUMAN DETECTION; MEAN FIELD METHODS; NON-REDUNDANT LOCAL BINARY PATTERNS; OBJECT REPRESENTATIONS; OCCLUSION REASONING; VARIATIONAL MEAN-FIELD THEORY;

ALGORITHMS; BAYESIAN NETWORKS; MEAN FIELD THEORY;

DETECTORS;

ALGORITHM; ANALYTIC METHOD; ARTICLE; BAYESIAN LEARNING; CLASSIFIER; DATA PROCESSING; HUMAN; HUMAN DETECTION; HYPOTHESIS; INTER OCCLUSION REASONING; MODEL; PRIORITY JOURNAL; SPATIAL ORIENTATION; THINKING; VARIATIONAL MEAN FIELD THEORY;

EID: 84876122150     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.12.009     Document Type: Article

References (40)

1. Enzweiler M., Gavrila D.M. Monocular pedestrian detection. survey and experiments. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31(12):2179-2195.
2. Dollár P., Wojek C., Schiele B., Perona P. Pedestrian detection. an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 34(4):743-761.
3. B. Wu, R. Nevatia, Cluster boosted tree classifier for multi-view, multi-pose object detection, in: Proceedings of the International Conference on Computer Vision, 2007, pp. 1-8.
4. B. Wu, R. Nevatia, Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors, in: Proceedings of the International Conference on Computer Vision, 2005, pp. 90-97.
5. Z. Lin, L.S. Davis, D. Doermann, D. DeMenthon, Hierarchical part-template matching for human detection and segmentation, in: Proceedings of the International Conference on Computer Vision, 2007, pp. 1-8.
6. D.M. Gavrila, V. Philomin, Real-time object detection for smart vehicles, in: Proceedings of the IEEE International on Computer Vision, vol. 1, 1999, pp. 87-93.
7. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, 2005, pp. 886-893.
8. Mohan A., Papageorgiou C., Poggio T. Example-based object detection in images by components. IEEE Trans. Pattern Anal. Mach. Intell. 2001, 23(4):349-361.
9. Gavrila D.M. A Bayesian, exemplar-based approach to hierarchical shape matching. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29(8):1-14.
10. D.T. Nguyen, W. Li, P. Ogunbona, An improved template matching method for object detection, in: Proceedings of the Asian Conference on Computer Vision, vol. 3, 2009, pp. 193-202.
11. Wu B., Nevatia R. Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors. Int. J. Comput. Vision 2007, 75(2):247-266.
12. J. Shotton, A. Blake, R. Cipolla, Contour-based learning for object detection, in: Proceedings of the International Conference on Computer Vision, vol. 1, 2005, pp. 503-510.
13. Ferrari V., Fevrier L., Jurie F., Schmid C. Groups of adjacent contour segments for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30(1):36-51.
14. Q. Zhu, S. Avidan, M.C. Yeh, K.T. Cheng, Fast human detection using a cascade of histograms of oriented gradients, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, 2006, pp. 1491-1498.
15. P. Sabzmeydani, G. Mori, Detecting pedestrians by learning shapelet features, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2007, pp. 1-8.
16. O. Tuzel, F. Porikli, P. Meer, Human detection via classification on riemannian manifolds, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2007, pp. 1-8.
17. W. Gao, H. Ai, S. Lao, Adaptive contour features in oriented granular space for human detection and segmentation, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2009, pp. 1786-1793.
18. P. Viola, M.J. Jones, Rapid object detection using a boosted cascade of simple features, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, 2001, pp. 511-518.
19. B. Leibe, E. Seemann, B. Schiele, Pedestrian detection in crowded scenes, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, 2005, pp. 878-885.
20. Y. Mu, S. Yan, Y. Liu, T. Huang, B. Zhou, Discriminative local binary patterns for human detection in personal album, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2008.
21. P. Ott, M. Everingham, Implicit color segmentation features for pedestrian and object detection, in: Proceedings of the International Conference on Computer Vision, 2009, pp. 723-730.
22. Viola P., Jones M.J., Snow D. Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vision 2005, 63(2):153-161.
23. Ojala T., Pietikǎinen M., Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 1996, 29(1):51-59.
24. X. Wang, T.X. Han, S. Yan, An HOG-LBP human detector with partial occlusion handling, in: Proceedings of the International Conference on Computer Vision, 2009, pp. 32-39.
25. M. Enzweiler, A. Eigenstetter, B. Schiele, D.M. Gavrila, Multi-cue pedestrian classification with partial occlusion handling, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2010, pp. 990-997.
26. Comaniciu D., Meer P. Mean shift. a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24(5):603-619.
27. P. Dollár, B. Babenko, S. Belongie, P. Perona, Z. Tu, Multiple component learning for object detection, in: Proceedings of the European Conference on Computer Vision, vol. 2, 2008, pp. 211-224.
28. Zhao T., Nevatia R., Wu B. Segmentation and tracking of multiple humans in crowded environments. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30(7):1198-1211.
29. Lin Z., Davis L.S. Shape-based human detection and segmentation via hierarchical part-template matching. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32(4):604-618.
30. C. Beleznai, H. Bischof, Fast human detection in crowded scenes by contour integration and local shape estimation, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2009, pp. 2246-2253.
31. C. Huang, R. Nevatia, High performance object detection by collaborative learning of joint ranking of granules features, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2010, pp. 41-48.
32. V.D. Shet, J. Neumann, V. Ramesh, L.S. Davis, Bilattice-based logical reasoning for human detection, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2007, pp. 1-8.
33. D.T. Nguyen, P. Ogunbona, W. Li, Human detection with contour-based local motion binary patterns, in: Proceedings of the IEEE Conference on Image Processing, 2011, pp. 3609-3612.
34. D.T. Nguyen, Z. Zong, W. Li, P. Ogunbona, Object detection using non-redundant local binary patterns, in: Proceedings of the IEEE Conference on Image Processing, 2010, pp. 4609-4612.
35. T.S. Jaakkola, Tutorial on Variational Approximation Methods, Technical Report, MIT Artificial Intelligence Laboratory, 2000.
36. Kullback S., Leibler R.A. On information and sufficiency. Ann. Math. Stat. 1951, 22:76-86.
37. http://pascal.inrialpes.fr/data/human/.
38. http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/.
39. http://www.eecs.qmul.ac.uk/~andrea/avss2007_d.html.
40. P. Felzenszwalb, D. McAllester, D. Ramanan, A discriminatively trained, multiscale, deformable part model, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.