Integrated pedestrian classification and orientation estimation

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

Volumn , Issue , 2010, Pages 982-989

  Enzweiler, Markus ^a   Gavrila, Dariu M ^b,c  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b DAIMLER AG   (Germany)

^c UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN; EVALUATION TECHNIQUES; MULTIPLE CAMERAS; ORIENTATION ESTIMATION; PERFORMANCE IMPROVEMENTS; PROBABILISTIC FRAMEWORK; PROBABILITY DENSITIES; REAL WORLD DATA;

COMPUTER VISION; PROBABILITY DENSITY FUNCTION;

ESTIMATION;

EID: 77955991799     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5540110     Document Type: Conference Paper

References (39)

1. S. Agarwal, A. Awan, and D. Roth. Learning to detect objects in images via a sparse, part-based representation. IEEE PAMI, 26(11):1475-1490, 2004.
2. S. Beauchemin and J. Barron. The computation of optical flow. ACM Computing Surveys, 27(3):433-467, 1995.
3. J. Berclaz, F. Fleuret, and P. Fua. Principled detection-by- classification from multiple views. Proc. of the Third Int. Conf. on Computer Vision Theory and Applications, pages 375-382, 2008.
4. U. Brefeld, P. Geibel, and F. Wysotzki. Support vector machines with example dependent costs. Proc. ECML, pages 23-34, 2003.
5. M. A. Carreira-Perpinan. Mode-finding for mixtures of Gaussian distributions. IEEE PAMI, 22(11):1318-1323, 2000.
6. T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. IEEE PAMI, 23(6):681-685, 2001.
7. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proc. CVPR, pages 886-893, 2005.
8. N. Dalal, B. Triggs, and C. Schmid. Human detection using oriented histograms of flow and appearance. In Proc. ECCV, pages 428-441, 2006.
9. P. Dollar, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: A benchmark. Proc. CVPR, 2009.
10. M. Enzweiler and D. M. Gavrila. A mixed generative-discriminative framework for pedestrian classification. In Proc. CVPR, 2008.
11. M. Enzweiler and D. M. Gavrila. Monocular pedestrian detection: Survey and experiments. IEEE PAMI, 31(12):2179-2195, 2009.
12. T. Gandhi and M. Trivedi. Image based estimation of pedestrian orientation for improving path prediction. In IEEE IV Symposium, pages 506-511, 2008.
13. D. M. Gavrila and S. Munder. Multi-cue pedestrian detection and tracking from a moving vehicle. IJCV, 73(1):41-59, 2007. (Pubitemid 46181616)
14. D. Geronimo, A. M. Lopez, A. D. Sappa, and T. Graf. Survey on pedestrian detection for advanced driver assistance systems. IEEE PAMI, available online: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.122, 2009.
15. T. Heap and D. Hogg. Improving specificity in PDMs using a hierarchical approach. In Proc. BMVC, pages 80-89. A. F. Clark (ed.), 1997.
16. M. Hofmann and D. M. Gavrila. Multi-view 3D human upper body pose estimation combining single-frame recovery, temporal integration and model adaptation. Proc. CVPR, 2009.
17. M. Hussein, F. Porikli, and L. Davis. A comprehensive evaluation framework and a comparative study for human detectors. IEEE ITS, 10(3):417-427, 2009.
18. A. K. Jain, R. P. W. Duin, and J. Mao. Statistical pattern recognition: A review. IEEE PAMI, 22(1):4-37, 2000.
19. E. Jones and S. Soatto. Layered active appearance models. In Proc. ICCV, pages 1097-1102, 2005.
20. B. Leibe, N. Cornelis, K. Cornelis, and L. V. Gool. Dynamic 3d scene analysis from a moving vehicle. In Proc. CVPR, 2007.
21. B. Leibe, E. Seemann, and B. Schiele. Pedestrian detection in crowded scenes. In Proc. CVPR, pages 878-885, 2005.
22. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
23. K. Mikolajczyk, C. Schmid, and A. Zisserman. Human detection based on a probabilistic assembly of robust part detectors. In Proc. ECCV, pages 69-81, 2004.
24. T. B. Moeslund and E. Granum. A survey of advances in vision-based human motion capture and analysis. CVIU, 103(2-3):90-126, 2006. (Pubitemid 44573873)
25. A. Mohan, C. Papageorgiou, and T. Poggio. Example-based object detection in images by components. IEEE PAMI, 23(4):349-361, 2001. (Pubitemid 32416300)
26. C. Nakajima, M. Pontil, B. Heisele, and T. Poggio. Fullbody recognition system. Pattern Recognition, 36:1997-2006, 2003.
27. C. Papageorgiou and T. Poggio. A trainable system for object detection. IJCV, 38:15-33, 2000.
28. J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances In Large Margin Classifiers, pages 61-74, 1999.
29. E. Seemann, M. Fritz, and B. Schiele. Towards robust pedestrian detection in crowded image sequences. In Proc. CVPR, 2007.
30. A. Shashua, Y. Gdalyahu, and G. Hayon. Pedestrian detection for driving assistance systems: Single-frame classification and system level performance. In IEEE IV Symposium, pages 1-6, 2004.
31. H. Shimizu and T. Poggio. Direction estimation of pedestrian from multiple still images. In IEEE IV Symposium, pages 596-600, 2004.
32. M. Turk and M. Kölsch. Perceptual interfaces. In G. Medioni and S. Kang, editors, Emerging Topics in Computer Vision. Prentice Hall, 2004.
33. O. Tuzel, F. Porikli, and P. Meer. Human detection via classification on Riemannian manifolds. In Proc. CVPR, 2007.
34. I. Ulusoy and C. M. Bishop. Generative versus discriminative methods for object recognition. In Proc. CVPR, pages 258-265, 2005.
35. P. Viola, M. Jones, and D. Snow. Detecting pedestrians using patterns of motion and appearance. IJCV, 63(2):153-161, 2005. (Pubitemid 40536820)
36. C. Wöhler and J. K. Anlauf. A time delay neural network algorithm for estimating image-pattern shape and motion. IVC, 17:281-294, 1999.
37. B. Wu and R. Nevatia. Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors. IJCV, 75(2):247-266, 2007.
38. L. Zhang, B. Wu, and R. Nevatia. Detection and tracking of multiple humans with extensive pose articulation. In Proc. ICCV, 2007.
39. Q. Zhu, S. Avidan, M. Ye, and K.-T. Cheng. Fast human detection using a cascade of histograms of oriented gradients. In Proc. CVPR, pages 1491-1498, 2006.