Fast multiple-part based object detection using KD-ferns

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

Volumn , Issue , 2013, Pages 947-954

  Levi, Dan ^a   Silberstein, Shai ^a   Bar Hillel, Aharon ^a  

^a GENERAL MOTORS CORPORATION   (United States)

Author keywords

Object Detection; Part based recognition; Pedestrian detection; Person detection

Indexed keywords

APPROXIMATE NEAREST NEIGHBOR SEARCH; OBJECT DETECTION; OBJECT DETECTION ALGORITHMS; PART-BASED RECOGNITION; PEDESTRIAN DETECTION; PERSON DETECTION; REAL-TIME IMPLEMENTATIONS; STATE-OF-THE-ART METHODS;

ALGORITHMS; BENCHMARKING; REAL TIME CONTROL; SIGNAL DETECTION;

OBJECT RECOGNITION;

EID: 84887384907     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.127     Document Type: Conference Paper

References (22)

1. A. Bar-Hillel, D. Levi, E. Krupka, and C. Goldberg. Partbased feature synthesis for human detection. In ECCV 2010, volume 6314, pages 127-142. 2010.
2. R. Benenson, M. Mathias, R. Timofte, and L. J. V. Gool. Pedestrian detection at 100 frames per second. In CVPR, pages 2903-2910. IEEE, 2012.
3. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
4. P. Dollár, R. Appel, and W. Kienzle. Crosstalk cascades for frame-rate pedestrian detection. In ECCV, 2012.
5. P. Dollár, S. Belongie, and P. Perona. The fastest pedestrian detector in the west. In BMVC, 2010.
6. P. Dollar, Z. Tu, P. Perona, and S. Belongie. Integral channel features. In BMVC, 2009.
7. P. Dollár, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: An evaluation of the state of the art. PAMI, 99, 2011.
8. C. Dubout and F. Fleuret. Exact acceleration of linear object detectors. In Proceedings of the European Conference on Computer Vision, 2012.
9. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2009 (VOC2009) Results. http://www. pascalnetwork. org/challenges/VOC/voc2009/workshop/index. html.
10. P. F. Felzenszwalb, R. B. Girshick, and D. A. McAllester. Cascade object detection with deformable part models. In CVPR, pages 2241-2248. IEEE, 2010.
11. P. F. Felzenszwalb, R. B. Girshick, D. A. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell., 32(9):1627-1645, 2010.
12. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale invariant learning. In CVPR, 2003.
13. J. H. Friedman, J. L. Bentley, and R. A. Finkel. An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw., 3(3):209-226, 1977.
14. K. Fukunaga and P. M. Narendra. A branch and bound algorithms for computing k-nearest neighbors. IEEE Trans. Computers, 24(7):750-753, 1975.
15. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60:91-110, 2004.
16. M. Muja and D. G. Lowe. Fast approximate nearest neighbors with automatic algorithm configuration. In VISAPP (1), pages 331-340, 2009.
17. M. Pedersoli, A. Vedaldi, and J. Gonzàlez. A coarse-to-fine approach for fast deformable object detection. In CVPR, pages 1353-1360. IEEE, 2011.
18. A. Shashua, Y. Gdalyahu, and G. Hayun. Pedestrian detection for driving assistance systems: Single-frame classification and system level performance. In intelligent vehicles symposium, pages 1-6, 2004.
19. C. Silpa-Anan and R. Hartley. Optimised kd-trees for fast image descriptor matching. In CVPR, 2008.
20. www. mobileye. com.
21. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. CVPR, 1:511, 2001.
22. C. Wojek and B. Schiele. A performance evaluation of single and multi-feature people detection. In G. Rigoll, editor, DAGM-Symposium, volume 5096 of Lecture Notes in Computer Science, pages 82-91. Springer, 2008