Learning SURF cascade for fast and accurate object detection

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2013, Pages 3468-3475

  Li, Jianguo ^a   Zhang, Yimin ^a  

^a INTEL CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE TEST; FASTER CONVERGENCE; LARGE-SCALE DATASET; LEARNING FRAMEWORKS; LOGISTIC REGRESSIONS; OBJECT DETECTION; PROCESSING SPEED; STATE-OF-THE-ART ALGORITHMS;

DECISION TREES; DETECTORS; FACE RECOGNITION; PERSONAL COMPUTERS;

OBJECT RECOGNITION;

EID: 84887340599     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.445     Document Type: Conference Paper

References (42)

1. S. Agarwal, A. Awan, and D. Roth. Learning to detect objects in images via a sparse, part-based representation. IEEE TPAMI, 26(11):1475-1490, 2004. 7
2. H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool. Surf: Speeded up robust features. CVIU, 110:346-359, 2008. 1, 2, 3
3. L. Bourdev and J. Brandt. Robust object detection via soft cascade. In CVPR, 2005. 1, 2, 6
4. S. Brubaker, J. Wu, J. Sun, and et al. On the design of cascades of boosted ensembles for face detection. IJCV, 2008. 2, 3, 6
5. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005. 2, 3
6. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. IJCV, 88(2):303-338, 2010. 7
7. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. Liblinear: A library for large linear classification. JMLR, 2008. 4
8. T. Fawcett. Roc graphs: Notes and practical considerations for researchers. Machine Learning, 2004. 2
9. L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In CVPR workshop, 2004. 5
10. P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In CVPR, 2008. 2
11. P. Felzenszwalb, R. Girshick, and D. McAllester. Cascade object detection with deformable part models. In CVPR, 2010. 2
12. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of statistics, 2000. 4
13. W. Gao, B. Cao, S. Shan, X. Chen, D. Zhou, X. Zhang, and D. Zhao. The cas-peal large-scale chinese face database and baseline evaluations. IEEE Trans. SMC-A, 38:149-161, 2008. 6
14. C. Huang, H. Ai, Y. Li, and S. Lao. Learning sparse features in granular space formulti-view face detection. In AFGR, 2006. 2
15. V. Jain and E. Learned-Miller. Fddb: A benchmark for face detection in unconstrained settings. Technical Report UM-CS-2010-009, UMass, 2010. http://vis-www. cs. umass. edu/fddb/. 6
16. V. Jain and E. Learned-Miller. Online domain adaptation of a pre-trained cascade of classifiers. In CVPR, 2011. 6
17. N. Kumar, P. N. Belhumeur, and S. K. Nayar. Facetracer: A search engine for large collections of images with faces. In ECCV, 2008. 5
18. I. Laptev. Improvements of object detection using boosted histograms. In BMVC, 2005. 3
19. J. Li, T. Wang, and Y. Zhang. Face detection using surf cascade. In ICCV workshop, 2011. 3, 6
20. S. Li, Z. Zhang, and et al. Floatboost learning for classification. In NIPS, 2002. 4
21. R. Lienhart and J. Maydt. An extended set of haar-like features for rapid object detection. In ICIP, 2002. 2, 5, 6
22. P. Long and R. Servedio. Boosting the area under the roc curve. In NIPS, 2007. 2
23. S. Maji and A. C. Berg. Max-margin additive classifiers for detection. In ICCV, 2009. 1
24. B. McCane and K. Novins. On training cascade face detectors. Image and Vision Computing, 2003. 2
25. K. Mikolajczyk, C. Schmid, and A. Zisserman. Human detection based on a probabilistic assembly of robust part detectors. In ECCV, 2004. 2, 6
26. T. Mita, T. Kaneko, and O. Hori. Joint haar-like features for face detection. In ICCV, 2005. 2
27. M.-T. Pham and T.-J. Cham. Fast training and selection of haar features during statistics in boosting-based face detection. In ICCV, 2007. 2
28. M.-T. Pham, Y. Gao, V. Hoang, and et al. Fast polygonal integration and its application in extending haar-like features to improve object detection. In CVPR, 2010. 1, 2, 6
29. P. J. Phillips, H. Moon, P. J. Rauss, and S. Rizvi. The feret evaluation methodology for face recognition algorithms. IEEE TPAMI, 22:1090-1104, 2000. 5, 6
30. T. Sim and S. Baker. The cmu pose, illumination, and expression (pie) database. In FG, 2002. 6
31. J. Sochman and J. Matas. Waldboost-learning for time constrained sequential detection. In CVPR, 2005. 1, 2
32. A. Sorokin and D. A. Forsyth. Utility data annotation with amazon mechanical turk. In CVPR Workshop, 2008. 1
33. V. Subburaman and S. Marcel. Fast bounding box estimation based face detection. In ECCV workshop, 2010. 6
34. O. Tuzel, F. Porikli, and P. Meer. Region covariance: A fast descriptor for detection and classification. In ECCV, 2006. 2
35. http://mplab. ucsd. edu. The MPLab GENKI Database. 5
36. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In CVPR, 2001. 1, 2, 4, 6
37. P. Viola and M. Jones. Robust real-time face detection. IJCV, 57(2):137-154, 2004. 1, 3, 6
38. S. Winder and M. Brown. Learning local image descriptors. In CVPR, 2007. 3
39. J. Wu and et al. Fast asymmetric learning for cascade face detection. IEEE PAMI, 2008. 1, 3
40. R. Xiao, H. Zhu, H. Sun, and X. Tang. Dynamic cascades for face detection. In ICCV, 2007. 1, 2, 4
41. C. Zhang and Z. Zhang. A survey of recent advances in face detection. Technical Report MSR-TR-2010-66, Microsoft Research, 2010. 1
42. Q. Zhu, S. Avidan, M.-C. Yeh, and K.-T. Cheng. Fast human detection using a cascade of histograms of oriented gradients. In CVPR, 2006. 1, 2, 4