Feature interaction descriptor for pedestrian detection

IEICE Transactions on Information and Systems

Volumn E93-D, Issue 9, 2010, Pages 2656-2659

  Cao, Hui ^a   Yamaguchi, Koichiro ^a   Ohta, Mitsuhiko ^a   Naito, Takashi ^a   Ninomiya, Yoshiki ^a  

^a Rd Management Section   (Japan)

Author keywords

Co uccurrence; Feaiure interaction; HOG; Pedestrian detection

Indexed keywords

GRAPHIC METHODS;

FEAIURE INTERACTION; FEATURE INTERACTIONS; HIGH-LEVEL FEATURES; OBJECT APPEARANCE; ORIENTED GRADIENTS; PAIRWISE INTERACTION; PEDESTRIAN DETECTION; SIMILARITY FUNCTIONS;

FEATURE EXTRACTION;

EID: 77956855041     PISSN: 09168532     EISSN: 17451361     Source Type: Journal    
DOI: 10.1587/transinf.E93.D.2656     Document Type: Article

References (16)

1. N. Dalai and B. Triggs, "Histograms of oriented gradients for human detection," CVPR, pp.886-893, 2005.
2. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," CVPR, 2001.
3. B. Wu and R. Nevatia, "Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors," TCCV, 2005.
4. Q. Zhu, S. Avidan, M. Yeh, and K. Cheng, "Fast human detection using a cascade of histograms of oriented gradients," CVPR, 2006.
5. O. Tuzel, F. Porikli, and P. Meer, "Human detection via classification on riemannian manifolds," CVPR, 2007.
6. A. Bosch, A. Zisserman, and X. Munoz, "Representing shape with a spatial pyramid kernel," CIVR, 2007.
7. T. Laptev, "Improving object detection with boosted histograms," Image Vis. Comput., vol.27, no.5, pp.535-544, 2009.
8. D. Geronimo, A. Lopez, D. Ponsa, and A.D. Sappa, "Haar wavelets and edge orientation histograms for on-board pedestrian detection," Proc. Iberian Conference on Pattern Recognition and Image Analysis, 2007.
9. B. Wu and R. Nevatia, "Optimizing discrimination-efficieney tradeoff in integrating heterogeneous local features for object detection," CVPR, 2008.
10. C. Papageorgiou and T. Poggio, "A trainable system for object detection," IJCV vol.38, no.l, 1533, 2000.
11. [l] A. Shashua, Y. Gdalyahu, and G. Hayun, "Pedestrian detection for driving assistance systems: Single-frame classification and system level performance," IEEE Intelligent Vehicles Symposium, 2004.
12. P. Felzenszwalb, D. Mc Allester and D. Ramanan, "A discrimina- tively trained, multiseale, deformable part model," CVPR, 2008.
13. T. Kobayashi and N. Otsu, "Image feature extraction using gradient local auto-correlations," ECCV, pp.346-358, Oct. 2008.
14. T. Watanabe, S. Ito, and K. Yokoi, "Co-occurrence histograms of oriented gradients for pedestrian detection," Proc. 3rd Pacific Rim Symposium on Advances in Image and Video Technology, pp.37- 47, 2009.
15. S. Munder and D.M. Gavrila, "An experimental study on pedestrian classification," IEEE Trans. Pattern Anal. Mach. Intell., vol.28, no. 11, pp. 1863-1868, 2006.
16. R.-E. Fan, K.-W. Chang, C.-I. Hsieh, X.-R. Wang, and C.-I. Lin, "LIBLINEAR: A library for large linear classification," J. Machine Learning Research, vol.9, pp. 187 l-l 874, 2008.