High performance object detection by collaborative learning of joint ranking of granules features

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

Volumn , Issue , 2010, Pages 41-48

  Huang, Chang ^a   Nevatia, Ram ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN COMBINATION; COLLABORATIVE LEARNING; DATA SETS; DETECTION ACCURACY; DISCRETE VALUES; DISCRIMINATIVE FEATURES; DYNAMIC SEARCH; FEATURE SELECTION; FEATURE SPACE; HIGH EFFICIENCY; OBJECT DETECTION; REAL-TIME DETECTION;

COMPUTATIONAL EFFICIENCY; COMPUTER VISION; GRANULATION; OBJECT RECOGNITION; SIMULATED ANNEALING;

FEATURE EXTRACTION;

EID: 77955987470     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5540230     Document Type: Conference Paper

References (22)

1. http://www.elec.qmul.ac.uk/staffinfo/andrea/avss2007-d.html.
2. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. CVPR, 2005.
3. G. Duan, C. Huang, H. Ai, and S. Lao. Boosting associated pairing comparison features for pedestrian detection. Ninth IEEE International Workshop on Visual Surveillance, 2009.
4. A. Ess, B. Leibe, and L. V. Gool. Depth and appearance for mobile scene analysis. ICCV, 2007.
5. P. Felzenszwalb. Learning models for object recognition. CVPR, 2001.
6. D. Gavrila. Pedestrian detection from a moving vehicle. ECCV, 2000.
7. C. Huang, H. Ai, Y. Li, and S. Lao. Learning sparse features in granular space for multi-view face detection. Proc. Seventh Intl Conf. Automatic Face and Gesture Recognition, 2006.
8. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, 1985.
9. B. Leibe, E. Seemann, and B. Schiele. Pedestrian detection in crowded scenes. CVPR, 2005.
10. C. Mikolajczyk, C. Schmid, and A. Zisserman. Human detection based on a probabilistic assembly of robust part detectors. ECCV, 2004.
11. A. Mohan, C. Papageorgiou, and T. Poggio. Example-based object detection in images by components. PAMI, 2001.
12. C. Papageorgiou, T. Evgeniou, and T. Poggio. A trainable pedestrian detection system. In Proceeding of Intelligent Vehicles, 1998.
13. R. E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 1999.
14. W. R. Schwartz, A. Kembhavi, D. Harwood, and L. S. Davis. Human detection using partial least squares analysis. ICCV, 2009.
15. O. Tuzel, F. Porikli, and P. Meer. Human detection via classification on riemannian manifolds. CVPR, 2007.
16. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. CVPR, 2001.
17. P. Viola, M. Jones, and D. Snow. Detecting pedestrians using patterns of motion and appearance. ICCV, 2003.
18. X. Wang, T. X. Han, and S. Yan. An hog-lbp human detector with partial occlusion handling. ICCV, 2009.
19. B. Wu and R. Nevatia. Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. ICCV, 2005.
20. B. Wu and R. Nevatia. Cluster boosted tree classifier for multi-view, multi-pose object detection. ICCV, 2007.
21. B. Wu and R. Nevatia. Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection. CVPR, 2008.
22. B. Wu, R. Nevatia, and Y. Li. Segmentation of multiple partially occluded objects by grouping merging assigning part detection responses. CVPR, 2008.