S. Bauer, S. Köhler, K. Doll, and U. Brunsmann, FPGA-GPU architecture for kernel SVM pedestrian detection, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Workshops, pp.61-68, 2010.
DOI : 10.1109/CVPRW.2010.5543772

R. Benenson, M. Mathias, R. Timofte, and L. Van-gool, Pedestrian detection at 100 frames per second, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.2903-2910, 2012.
DOI : 10.1109/CVPR.2012.6248017

R. Benenson, M. Omran, J. Hosang, and B. Schiele, Ten Years of Pedestrian Detection, What Have We Learned?, pp.613-627, 2014.
DOI : 10.1007/978-3-319-16181-5_47

C. Blair, N. Robertson, and D. Hume, Characterizing a heterogeneous system for person detection in video using histograms of oriented gradients: Power versus speed versus accuracy. Emerging and Selected Topics in Circuits and Systems, IEEE Journal on, vol.3, issue.2, pp.236-247, 2013.

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

P. Dollar, C. Wojek, B. Schiele, and P. Perona, Pedestrian detection: An evaluation of the state of the art. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.34, issue.4, pp.743-761, 2012.

M. Enzweiler and D. Gavrila, Monocular pedestrian detection: Survey and experiments. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.12, pp.312179-2195, 2009.

M. Everingham, L. Van-gool, C. Williams, J. Winn, and A. Zisserman, The Pascal Visual Object Classes (VOC) Challenge, International Journal of Computer Vision, vol.73, issue.2, pp.303-338, 2010.
DOI : 10.1007/s11263-009-0275-4

T. Gandhi and M. Trivedi, Pedestrian protection systems: Issues, survey, and challenges. Intelligent Transportation Systems, IEEE Transactions, pp.413-430, 2007.

D. Gavrila and S. Munder, Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle, International Journal of Computer Vision, vol.10, issue.6, pp.41-59, 2007.
DOI : 10.1007/s11263-006-9038-7

M. Hahnle, F. Saxen, M. Hisung, U. Brunsmann, and K. Doll, FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.629-635, 2013.
DOI : 10.1109/CVPRW.2013.95

M. Happe, E. Lübbers, and M. Platzner, A self-adaptive heterogeneous multi-core architecture for embedded real-time video object tracking, Journal of Real-Time Image Processing, vol.50, issue.2, pp.95-110, 2013.
DOI : 10.1007/s11554-011-0212-y

C. Hartmann, A. Yupatova, M. Reichenbach, D. Fey, and R. German, A holistic approach for modeling and synthesis of image processing applications for heterogeneous computing architectures, 2015.

G. Hong, B. Kim, Y. Hwang, and K. Kwon, Fast multi-feature pedestrian detection algorithm based on histogram of oriented gradient using discrete wavelet transform, Multimedia Tools and Applications, vol.9, issue.3, 2015.
DOI : 10.1007/s11042-015-2455-2

C. Hua, Y. Makihara, Y. Yagi, S. Iwasaki, K. Miyagawa et al., Onboard monocular pedestrian detection by combining spatio-temporal hog with structure from motion algorithm, Machine Vision and Applications, vol.57, issue.8, pp.161-183, 2015.
DOI : 10.1007/s00138-014-0653-y

M. Hussein, F. Porikli, and L. Davis, A comprehensive evaluation framework and a comparative study for human detectors. Intelligent Transportation Systems, IEEE Transactions on, vol.10, issue.3, pp.417-427, 2009.

G. Inggs, D. Thomas, and W. Luk, An efficient, automatic approach to high performance heterogeneous computing. arXiv preprint arXiv, p.150504417, 2015.

T. Joachims, R. Kadota, H. Sugano, M. Hiromoto, H. Ochi et al., Svmlight: Support vector machine . http://svmlight.joachims.org/ 19 Hardware architecture for hog feature extraction, Intelligent Information Hiding and Multimedia Signal Processing IIH-MSP '09. Fifth International Conference on, pp.1330-1333, 1999.

S. Kim, J. Kim, V. Wan, and I. Suh, Automotive adas camera system configuration using multi-core microcontroller. Tech. rep., SAE Technical Paper 21 A spatiotemporal descriptor based on 3d-gradients, BMVC 2008-19th British Machine Vision Conference , British Machine Vision Association, pp.275-276, 2008.

W. Liu, B. Yu, C. Duan, L. Chai, H. Yuan et al., A pedestrian-detection method based on heterogeneous features and ensemble of multi-view?pose parts. Intelligent Transportation Systems, IEEE Transactions on, vol.16, issue.2, pp.813-824, 2015.

X. Ma, W. Najjar, and A. Roy-chowdhury, Evaluation and acceleration of high-throughput fixedpoint object detection on FPGAs. Circuits and Systems for Video Technology, IEEE Transactions on, vol.25, issue.6, pp.1051-1062, 2015.

T. Machida and T. Naito, GPU & CPU cooperative accelerated pedestrian and vehicle detection, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp.506-513, 2011.
DOI : 10.1109/ICCVW.2011.6130285

L. Maggiani, C. Salvadori, M. Petracca, P. Pagano, and R. Saletti, Reconfigurable architecture for computing histograms in real-time tailored to FPGA-based smart camera, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), pp.1042-1046, 2014.
DOI : 10.1109/ISIE.2014.6864756
URL : https://hal.archives-ouvertes.fr/hal-01205924

L. Maggiani, C. Bourrasset, F. Berry, J. Sérot, M. Petracca et al., Parallel image gradient extraction core for FPGA-based smart cameras, Proceedings of the 9th International Conference on Distributed Smart Camera, ICDSC '15, pp.128-133, 2015.
DOI : 10.1145/2789116.2789139
URL : https://hal.archives-ouvertes.fr/hal-01199197

L. Maggiani, C. Bourrasset, M. Petracca, F. Berry, P. Pagano et al., HOG-Dot: A Parallel Kernel-Based Gradient Extraction for Embedded Image Processing, IEEE Signal Processing Letters, vol.22, issue.11, pp.2132-2136, 2015.
DOI : 10.1109/LSP.2015.2463092
URL : https://hal.archives-ouvertes.fr/hal-01220417

T. Ojala, M. Pietikäinen, and D. Harwood, A comparative study of texture measures with classification based on featured distributions, Pattern Recognition, vol.29, issue.1, pp.51-59, 1996.
DOI : 10.1016/0031-3203(95)00067-4

J. Quinton and B. Girau, Predictive neural fields for improved tracking and attentional properties, The 2011 International Joint Conference on Neural Networks, pp.1629-1636, 2011.
DOI : 10.1109/IJCNN.2011.6033420
URL : https://hal.archives-ouvertes.fr/inria-00603902

O. Reiche, K. Haublein, M. Reichenbach, F. Hannig, J. Teich et al., Automatic optimization of hardware accelerators for image processing, DATE Workshop on Heterogeneous Architectures and Design Methods for Embedded Image Systems, 2015.

J. Tanabe, S. Toru, Y. Yamada, T. Watanabe, M. Okumura et al., 18.2 a 1.9 tops and 564gops/w heterogeneous multicore soc with color-based object classification accelerator for image-recognition applications, Solid-State Circuits Conference(ISSCC ), pp.1-3, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00007462

B. Vangel, C. Torres-huitzil, and B. Girau, Randomly Spiking Dynamic Neural Fields, ACM Journal on Emerging Technologies in Computing Systems, vol.11, issue.4, p.37, 2015.
DOI : 10.1145/2629517
URL : https://hal.archives-ouvertes.fr/hal-01071862

V. Vapnik, Estimation of Dependences Based on Empirical Data World Health Organization (2013) Road traffic injuries, 1982.

S. Wu, R. Laganì-ere, and P. Payeur, Improving pedestrian detection with selective gradient self-similarity feature, Pattern Recognition, vol.48, issue.8, pp.2364-2376, 2015.
DOI : 10.1016/j.patcog.2015.01.005

R. Yadav, V. Senthamilarasu, K. Kutty, V. Vaidya, S. Ugale et al., A review on day-time pedestrian detection . Tech. rep., SAE Technical Paper 37 A new pedestrian detection method based on combined HOG and LSS features, Neurocomputing, vol.151, pp.1006-1014, 2015.

J. Zhang, K. Huang, Y. Yu, and T. Tan, Boosted local structured HOG-LBP for object localization, CVPR 2011, pp.1393-1400, 2011.
DOI : 10.1109/CVPR.2011.5995678