P. V. Hough, Method and means for recognizing complex patterns, 1962.

B. Leibe and B. Schiele, Interleaving Object Categorization and Segmentation, 2006.
DOI : 10.1007/11414353_10

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

F. Moosmann, E. Nowak, and F. Jurie, Randomized Clustering Forests for Image Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.9, pp.1632-1646, 2008.
DOI : 10.1109/TPAMI.2007.70822

URL : https://hal.archives-ouvertes.fr/inria-00548666

J. Gall and V. Lempitsky, Class-specific hough forests for object detection In Decision forests for computer vision and medical image analysis, pp.143-157, 2013.

A. Ciolini, L. Seidenari, S. Karaman, and A. D. Bimbo, Efficient hough forest object detection for low-power devices, 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp.1-6, 2015.
DOI : 10.1109/ICMEW.2015.7169857

Y. Murai, Y. Yamauchi, T. Yamashita, and H. Fujiyoshi, Weighted Hough Forest for object detection, 2015 14th IAPR International Conference on Machine Vision Applications (MVA), pp.122-125, 2015.
DOI : 10.1109/MVA.2015.7153148

J. Gall, N. Razavi, and L. Van-gool, An Introduction to Random Forests for Multi-class Object Detection, Outdoor and Large- Scale Real-World Scene Analysis, pp.243-263, 2012.
DOI : 10.1007/978-3-642-34091-8_11

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.886-893, 2005.
DOI : 10.1109/CVPR.2005.177

URL : https://hal.archives-ouvertes.fr/inria-00548512

Y. Cheng, Mean shift, mode seeking, and clustering. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.17, issue.8, pp.790-799, 1995.

A. Bastian-leibe, B. Leonardis, and . Schiele, Robust Object Detection with Interleaved Categorization and Segmentation, International Journal of Computer Vision, vol.73, issue.2, pp.259-289, 2008.
DOI : 10.1007/s11263-007-0095-3

T. Lindeberg, Feature detection with automatic scale selection, International Journal of Computer Vision, vol.30, issue.2, pp.79-116, 1998.
DOI : 10.1023/A:1008045108935

N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, pp.285-29623, 1975.
DOI : 10.1109/TSMC.1979.4310076

S. Agarwal, A. Awan, and D. Roth, Learning to detect objects in images via a sparse, part-based representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.26, issue.11, pp.1475-1490, 2004.

M. Andriluka, S. Roth, and B. Schiele, People-trackingby-detection and people-detection-by-tracking, Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/cvpr.2008.4587583

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

J. Gall, A. Yao, N. Razavi, L. Van-gool, and V. Lempitsky, Hough Forests for Object Detection, Tracking, and Action Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.11, pp.2188-2202, 2011.
DOI : 10.1109/TPAMI.2011.70

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=