R. Benenson, M. Omran, J. Hosang, and B. Schiele, Ten years of pedestrian detection, what we have learnt, Workshop of Europ. Conf. on Computer Vision (ECCV'14), 2014.

L. Bourdev and J. Brandt, Robust Object Detection via Soft Cascade, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.236-243, 2005.
DOI : 10.1109/CVPR.2005.310
URL : http://www.lubomir.org/academic/softcascade.pdf

M. Breitenstein, F. Reichlin, B. Leibe, E. Koller-meier, and L. Van-gool, Online multi-person tracking-by-detection from a single, uncalibrated camera, IEEE Trans. on Pattern Analysis and Machine Intelligence, issue.9, pp.331820-1833, 2011.
DOI : 10.1109/tpami.2010.232

J. Cao, Y. Pang, and X. Li, Pedestrian detection inspired by appearance constancy and shape symmetry, Int. Conf. on Computer Vision and Pattern Recognition (CVPR'16), pp.1316-1324, 2016.
DOI : 10.1109/cvpr.2016.147
URL : http://arxiv.org/pdf/1511.08058

J. Cao, Y. Pang, and X. Li, Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry, IEEE Transactions on Image Processing, vol.25, issue.12, pp.5538-5551, 2016.
DOI : 10.1109/TIP.2016.2609807
URL : http://arxiv.org/pdf/1511.08058

C. Chen, T. Chen, D. Wang, and T. Chen, A cost-effective people-counter for a crowd of moving people based on two-stage segmentation, Journal of Information Hiding and Multimedia Signal Processing, vol.3, issue.1, pp.12-23, 2012.

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. Dollár, Piotr's Computer Vision Matlab Toolbox (PMT), https://github

P. Dollár, R. Appel, S. Belongie, and P. Perona, Fast Feature Pyramids for Object Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.8, pp.361532-1545, 2014.
DOI : 10.1109/TPAMI.2014.2300479

P. Dollár, Z. Tu, H. Tao, and S. Belongie, Feature Mining for Image Classification, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383046

P. Dollar, C. Wojek, B. Schiele, and P. Perona, Pedestrian detection: A benchmark, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.304-311, 2009.
DOI : 10.1109/CVPR.2009.5206631

P. Dollár, C. Wojek, B. Schiele, and P. Perona, Pedestrian Detection: An Evaluation of the State of the Art, PAMI'12), pp.743-761, 2012.
DOI : 10.1109/TPAMI.2011.155

A. Ess, K. Schindler, B. Leibe, and L. Van-gool, Object Detection and Tracking for Autonomous Navigation in Dynamic Environments, The International Journal of Robotics Research, vol.1, issue.14, pp.291707-1725, 2010.
DOI : 10.1109/TPAMI.2007.70770
URL : http://europa.informatik.uni-freiburg.de/files/ess-autonomousnavigation-ijrr10final.pdf

M. Everingham, L. Van-gool, C. K. 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 : 10.1371/journal.pcbi.0040027
URL : http://eprints.pascal-network.org/archive/00006961/01/everingham10.pdf

M. Garey and D. Johnson, Computers and intractability: A guide to the theory of np-completeness, 1979.

D. Gerónimo, A. López, A. Sappa, and T. Graf, Survey of Pedestrian Detection for Advanced Driver Assistance Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.7, pp.321239-1258, 2010.
DOI : 10.1109/TPAMI.2009.122

B. Han and X. Wang, Detection for Power line Inspection, MATEC Web of Conferences, vol.57, issue.2, p.3010, 2017.
DOI : 10.1109/CVPR.2005.310
URL : https://www.matec-conferences.org/articles/matecconf/pdf/2017/14/matecconf_gcmm2017_03010.pdf

J. Hosang, M. Omran, R. Benenson, and B. Schiele, Taking a deeper look at pedestrians, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4073-4082, 2015.
DOI : 10.1109/CVPR.2015.7299034
URL : http://arxiv.org/pdf/1501.05790

L. Jourdheuil, N. Allezard, T. Chateau, and T. Chesnais, Heterogeneous Adaboost with real-time constraints -application to the detection of pedestrians by stereovision, Int. Conf. on Computer Vision Theory and Applications, pp.539-546, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01296009

L. Leal-taixé, A. Milan, I. Reid, S. Roth, and K. Schindler, Towards a benchmark for multitarget tracking, ArXiv:1504, p.1942, 2015.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed et al., SSD: Single Shot MultiBox Detector, Europ. Conf. on Computer Vision (ECCV'16), 2016.
DOI : 10.1109/CVPR.2008.4587597
URL : http://arxiv.org/pdf/1512.02325

A. A. Mekonnen, F. Lerasle, A. Herbulot, and C. Briand, People Detection with Heterogeneous Features and Explicit Optimization on Computation Time, 2014 22nd International Conference on Pattern Recognition, 2014.
DOI : 10.1109/ICPR.2014.741
URL : https://hal.archives-ouvertes.fr/hal-01059551

S. Ren, K. He, R. Girshick, S. Sun, and R. Faster, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, PAMI'17), pp.1137-1149, 2017.
DOI : 10.1109/TPAMI.2016.2577031
URL : http://arxiv.org/pdf/1506.01497

R. E. Schapire, The boosting approach to machine learning: An overview, Lecture Notes in Statistics, pp.149-172, 2003.

J. Sochman and J. Matas, WaldBoost ??? Learning for Time Constrained Sequential Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005.
DOI : 10.1109/CVPR.2005.373
URL : http://www.vis.uky.edu/~dnister/Teaching/CS684Fall2005/sochman-waldboost-cvpr05.pdf

M. Teutsch and W. Krüger, Robust and fast detection of moving vehicles in aerial videos using sliding windows, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.26-34, 2015.
DOI : 10.1109/CVPRW.2015.7301396

Y. Tian, P. Luo, X. Wang, and X. Tang, Pedestrian detection aided by deep learning semantic tasks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5079-5087, 2015.
DOI : 10.1109/CVPR.2015.7299143
URL : http://arxiv.org/pdf/1412.0069

R. Varga and S. Nedevschi, Robust Pallet Detection for Automated Logistics Operations, Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp.470-477, 2016.
DOI : 10.5220/0005674704700477

P. Viola and M. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, vol.57, issue.2, pp.137-154, 2004.
DOI : 10.1023/B:VISI.0000013087.49260.fb
URL : http://csdl.computer.org/comp/proceedings/iccv/2001/1143/02/114320747.pdf

R. Xiao, L. Zhu, and H. Zhang, Boosting chain learning for object detection, Int. Conf. on Computer Vision (ICCV'03), 2003.

C. Zhang and P. Viola, Multiple-instance pruning for learning efficient cascade detectors, Neural Information Processing Systems (NIPS'08), pp.1681-1688, 2008.

L. Zhang, L. Lin, X. Liang, and K. He, Is Faster R-CNN Doing Well for Pedestrian Detection?, European Conf. on Computer Vision (ECCV'16), pp.443-457, 2016.
DOI : 10.1109/CVPR.2015.7298784
URL : http://arxiv.org/pdf/1607.07032

M. Zhang and R. Alhajj, Content-based image retrieval: From the object detection/recognition point of view, In Artificial Intelligence for Maximizing Content Based Image Retrieval, ser. PA: Information Science Reference, Z. Ma, pp.115-144, 2009.

S. Zhang, R. Benenson, M. Omran, J. Hosang, and B. Schiele, How Far are We from Solving Pedestrian Detection?, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1259-1267, 2016.
DOI : 10.1109/CVPR.2016.141
URL : http://arxiv.org/pdf/1602.01237

S. Zhang, R. Benenson, and B. Schiele, Filtered channel features for pedestrian detection, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1751-1760, 2015.
DOI : 10.1109/CVPR.2015.7298784
URL : http://arxiv.org/pdf/1501.05759

Q. Zhu, M. Yeh, K. Cheng, and S. Avidan, Fast human detection using a cascade of histograms of oriented gradients, Int. Conf. on Computer Vision and Pattern Recognition (CVPR'06), 2006.