Combination features and models for human detection

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

Volumn 07-12-June-2015, Issue , 2015, Pages 240-248

  Jiang, Yunsheng ^a   Ma, Jinwen ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; COMPUTER VISION; PATTERN RECOGNITION;

COMBINATION MODELS; COMPLEMENTARY FEATURES; COMPUTATION COSTS; DEFORMABLE PART-BASED MODELS; DETECTION PERFORMANCE; FUSION ALGORITHMS; OBJECT CATEGORIES; PASCAL VOC DATASETS;

FEATURE EXTRACTION;

EID: 84958980901     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298620     Document Type: Conference Paper

References (33)

1. S. Belongie,. T. Malik, and. T. Puzicha. Matching shapes. Tn IEEE Int'l Can! on Computer Vision (ICCV), volume I, pages 454-461. IEEE, 2001
2. L. Bourdev. Poselets and their applications in high-level computer vision. http://www. cs. berkeley. edu/-lbourdev/poselets. 7
3. L. Bourdev, S. Maji, T. Brox, and J. Malik. Detecting people using mutually consistent poselet activations. In European Con[. on Computer Vision (ECCV), pages 168-181. Springer, 2010
4. L. Bourdev and J. Malik. Poselets: Body part detectors trained using 3D human pose annotations. In IEEE Int'l Con! on Computer Vision (ICCV), pages 1365-1372. IEEE, 2009
5. Y.-T. Chen and c.-S. Chen. Fast human detection using a novel boosted cascading structure with meta stages. IEEE Trans. on Image Processing (TIP), 17(8):1452-1464, 2008
6. N. Dalal. Finding people in images and videos. PhD thesis, Institut National Poly technique de Grenoble-INPG, 2006
7. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In IEEE Con! on Computer Vision and Pattern R ecognition (CVPR), volume I, pages 886-893. IEEE, 2005
8. T. Endres, K. J. Shih, J. Jiaa, and D. Hoiem. Learning collections of part models for object recognition. In IEEE Con! on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013
9. M. Everingham, L. Van Gool, C. K. T. Williams, J. Winn, and A. Zisserman. The pascal visual object classes challenge workshop 2010. h t t p: //pascallin. ecs. soton. ac. uk/challen ges/vOC/voc2010/workshop/in dex. html. 8
10. P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. Tn IEEE Con! on Computer Vision and Pattern Recognition (CVPR), pages 1-8. IEEE, 2008
11. P. F. Felzenszwalb, R. B. Girshick, and D. McAllester. Cascade object detection with deformable part models. Tn IEEE Con! on Computer Vision and Pattern R ecognition (CVPR), pages 2241-2248. IEEE, 2010
12. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAM!), 32(9): 1627-1645, 20 I O. I, 2, 6, 8
13. P. F. Felzenszwalb and D. McAllester. Object detection grammars. Tn IEEE Int'l Con! on Computer Vision (ICCV) Workshops, page 691. IEEE, 2011
14. R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. Tn IEEE Con[. on Computer Vision and Pattern Recognition (CVPR), 2014
15. R. B. Girshick, P. F. Felzenszwalb, and D. A. McAllester. Object detection with grammar models. Tn Advances in Neural Information Processing S ystems (NIPS), pages 442-450, 2011
16. G. Gkioxari, B. Hariharan, R. Girshick, and J. Malik. Using k-poselets for detecting people and localizing their keypoints. Tn IEEE Con[. on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014
17. D. H. Hubel. Eye, brain, and vision. Scientific American Library/Scientific American Books, 1995
18. S. Ioffe and D. Forsyth. Mixtures of trees for object recognition. Tn IEEE Con[. on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 180-185. IEEE, 2001
19. F. S. Khan, R. M. Anwer, J. v. d. Weijer, A. D. Bagdanov, M. Vanrell, and A. M. Lopez. Color attributes for object detection. Tn IEEE Con[. on Computer Vision and Pattern R ecognition (CVPR). IEEE, 2012
20. J. Kokkinos. Rapid deformable object detection using dualtree branch-and-bound. Tn Advances in Neural Information Processing Systems (NIPS), pages 2681-2689, 2011
21. T. Kokkinos. Bounding part scores for rapid detection with deformable part models. In European Con! on Computer Vision (ECCV) Workshops and Demonstrations, pages 41-50. Springer, 2012
22. R. Lienhart and J. Maydt. An extended set of haar-like features for rapid object detection. Tn IEEE Int'l Con[. on Image Processing (ICIP), volume 1, pages 900-903. IEEE, 2002
23. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International fournal of Computer Vision (IlCV), 60(2):91-110, 2004. I
24. T. Ojala, M. Pietikainen, and D. Harwood. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In IEEE Int'l Con! on Pattern R ecognition (ICPR), volume 1, pages 582-585. IEEE, 1994. I
25. X. Ren and D. Ramanan. Histograms of sparse codes for object detection. Tn IEEE Con[. on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013
26. O. Tuzel, F. Porikli, and P. Meer. Region covariance: A fast descriptor for detection and classification. In European Con! on Computer Vision (ECCV) Workshops and Demonstrations. Springer, 2006
27. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman. Multiple kernels for object detection. In IEEE Int'l Con! on Computer Vision (ICCV). IEEE, 2009
28. P. Viola and M. J. Jones. Robust real-time face detection. International fournal of Computer Vision (IlCV), 57(2):137-154, 2004
29. P. Viola, M. J. Jones, and D. Snow. Detecting pedestrians using patterns of motion and appearance. Tn IEEE Int 'l Con[. on Computer Vision (ICCV), pages 734-741. IEEE, 2003
30. X. Wang, T. X. Han, and S. Yan. An HOG-LBP human detector with partial occlusion handling. In IEEE Int'l Con! on Computer Vision (ICCV), pages 32-39. IEEE, 2009. I
31. X. Wang, M. Yang, S. Zhu, and Y. Lin. Regionlets for generic object detection. Tn IEEE Int'l Con! on Computer Vision (ICCV). IEEE, 2013
32. J. Zhang, K. Huang, Y. Yu, and T. Tan. Boosted local structured HOG-LBP for object localization. In IEEE Con! on Computer Vision and Pattern Recognition (CVPR), pages 1393-1400. IEEE, 2011. I, 8
33. J. Zhang, Y. Huang, K. Huang, Z. Wu, and T. Tan. Data decomposition and spatial mixture modeling for part based model. In Asian Con! on Computer Vision (ACCV), pages 123-137. Springer, 2013.