C. Zhang, H. Li, X. Wang, and X. Yang, Cross-scene crowd counting via deep convolutional neural networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298684

B. Wu and R. Nevatia, Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors, ICCV, 2005.

P. Viola, M. J. Jones, and D. Snow, Detecting pedestrians using patterns of motion and appearance, pp.153-161, 2003.
DOI : 10.1109/iccv.2003.1238422
URL : http://www.merl.com/papers/docs/TR2003-90.pdf

G. J. Brostow and R. Cipolla, Unsupervised Bayesian Detection of Independent Motion in Crowds, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), 2006.
DOI : 10.1109/CVPR.2006.320
URL : http://mi.eng.cam.ac.uk/reports/svr-ftp/brostow_MotionInCrowdsCVPR06.pdf

V. Rabaud and S. Belongie, Counting Crowded Moving Objects, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), 2006.
DOI : 10.1109/CVPR.2006.92

A. B. Chan, Z. J. Liang, and N. Vasconcelos, Privacy preserving crowd monitoring: Counting people without people models or tracking, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587569
URL : http://www.svcl.ucsd.edu/publications/conference/2008/cvpr08/cvpr08_peoplecnt.pdf

K. Chen, C. C. Loy, S. Gong, and T. Xiang, Feature Mining for Localised Crowd Counting, Procedings of the British Machine Vision Conference 2012, 2012.
DOI : 10.5244/C.26.21
URL : http://www.bmva.org/bmvc/2012/BMVC/paper021/paper021.pdf

D. Ryan, S. Denman, C. Fookes, and S. Sridharan, Crowd Counting Using Multiple Local Features, 2009 Digital Image Computing: Techniques and Applications, 2009.
DOI : 10.1109/DICTA.2009.22

H. Idrees, I. Saleemi, C. Seibert, and M. Shah, Multi-source Multi-scale Counting in Extremely Dense Crowd Images, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.329
URL : http://crcv.ucf.edu/papers/cvpr2013/Counting_V3o.pdf

D. Kong, D. Gray, and H. Tao, Counting Pedestrians in Crowds Using Viewpoint Invariant Training, Procedings of the British Machine Vision Conference 2005, 2005.
DOI : 10.5244/C.19.63

V. Lempitsky and A. Zisserman, Learning to count objects in images, NIPS, 2010.

D. Onoro-rubio and R. J. López-sastre, Towards Perspective-Free Object Counting with Deep Learning, ECCV, 2016.
DOI : 10.1109/DICTA.2009.22

Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, Single-Image Crowd Counting via Multi-Column Convolutional Neural Network, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.70

D. B. Sam, S. Surya, and R. V. Babu, Switching Convolutional Neural Network for Crowd Counting, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
DOI : 10.1109/CVPR.2017.429
URL : http://arxiv.org/pdf/1708.00199

X. Gao, X. Hou, J. Tang, and H. Cheng, Complete solution classification for the perspective-three-point problem, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.8, pp.930-943, 2003.

C. Arteta, V. Lempitsky, and A. Zisserman, Counting in the Wild, ECCV, 2016.
DOI : 10.1109/CVPR.2015.7298684

A. B. Chan and N. Vasconcelos, Counting People With Low-Level Features and Bayesian Regression, IEEE Transactions on Image Processing, vol.21, issue.4, pp.2160-2177, 2012.
DOI : 10.1109/TIP.2011.2172800
URL : http://www.svcl.ucsd.edu/publications/journal/2011/peoplecount/tip_achan.pdf

N. C. Tang, Y. Lin, M. Weng, and H. M. Liao, Cross-Camera Knowledge Transfer for Multiview People Counting, IEEE Transactions on Image Processing, vol.24, issue.1, pp.80-93, 2015.
DOI : 10.1109/TIP.2014.2363445

S. Huang, X. Li, Z. Zhang, F. Wu, S. Gao et al., Body Structure Aware Deep Crowd Counting, IEEE Transactions on Image Processing, vol.27, issue.3, pp.1049-1059, 2018.
DOI : 10.1109/TIP.2017.2740160

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, NIPS, 2012.
DOI : 10.1162/neco.2009.10-08-881
URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

E. Walach and L. Wolf, Learning to Count with CNN Boosting, ECCV, 2016.
DOI : 10.1109/TPAMI.2008.132

V. A. Sindagi and V. M. Patel, Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs, 2017 IEEE International Conference on Computer Vision (ICCV), 2017.
DOI : 10.1109/ICCV.2017.206
URL : http://arxiv.org/pdf/1708.00953

, Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting, AVSS, 2017.

F. Xiong, X. Shi, and D. Yeung, Spatiotemporal modeling for crowd counting in videos, ICCV, 2017.

J. Liu, C. Gao, D. Meng, and A. G. Hauptmann, Decidenet: Counting varying density crowds through attention guided detection and density estimation, CVPR, 2018.

D. Ciregan, U. Meier, and J. Schmidhuber, Multi-column deep neural networks for image classification, CVPR, 2012.

M. Wang and X. Wang, Automatic adaptation of a generic pedestrian detector to a specific traffic scene, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995698
URL : http://mmlab.ie.cuhk.edu.hk/archive/2011/adaptDet.pdf

R. Stewart, M. Andriluka, and A. Y. Ng, End-to-End People Detection in Crowded Scenes, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.255
URL : http://arxiv.org/pdf/1506.04878

P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001.
DOI : 10.1109/CVPR.2001.990517
URL : http://www.cc.gatech.edu/ccg/./paper_of_week/viola01rapid.pdf

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), 2005.
DOI : 10.1109/CVPR.2005.177
URL : https://hal.archives-ouvertes.fr/inria-00548512

S. Lin, J. Chen, and H. Chao, Estimation of number of people in crowded scenes using perspective transformation, TSMC-A, vol.31, issue.6, pp.645-654, 2001.

C. S. Regazzoni and A. Tesei, Distributed data fusion for real-time crowding estimation, Signal Processing, vol.53, issue.1, pp.47-63, 1996.
DOI : 10.1016/0165-1684(96)00075-8

A. Marana, L. D. Costa, R. Lotufo, and S. Velastin, On the efficacy of texture analysis for crowd monitoring, Proceedings SIBGRAPI'98. International Symposium on Computer Graphics, Image Processing, and Vision (Cat. No.98EX237), 1998.
DOI : 10.1109/SIBGRA.1998.722773

N. Paragios and V. Ramesh, A MRF-based approach for real-time subway monitoring, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001.
DOI : 10.1109/CVPR.2001.990644
URL : http://www.mas.ecp.fr/Personnel/nikos/pub/cvpr01.pdf

M. Fu, P. Xu, X. Li, Q. Liu, M. Ye et al., Fast crowd density estimation with convolutional neural networks, Engineering Applications of Artificial Intelligence, vol.43, pp.81-88, 2015.
DOI : 10.1016/j.engappai.2015.04.006

C. Wang, H. Zhang, L. Yang, S. Liu, and X. Cao, Deep People Counting in Extremely Dense Crowds, Proceedings of the 23rd ACM international conference on Multimedia, MM '15, 2015.
DOI : 10.1109/ICCV.2003.1238663

L. Boominathan, S. S. Kruthiventi, and R. V. Babu, CrowdNet, Proceedings of the 2016 ACM on Multimedia Conference, MM '16, 2016.
DOI : 10.1109/CVPR.2015.7298684

Z. Zhao, H. Li, R. Zhao, and X. Wang, Crossing-Line Crowd Counting with Two-Phase Deep Neural Networks, ECCV, 2016.
DOI : 10.1109/CVPR.2015.7298684

S. Kumagai, K. Hotta, and T. Kurita, Mixture of counting CNNs, Machine Vision and Applications, vol.19, issue.1, 2017.
DOI : 10.1109/CVPR.2016.70

M. Marsden, K. Mcguiness, S. Little, and N. E. Connor, Fully Convolutional Crowd Counting on Highly Congested Scenes, Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, p.2017
DOI : 10.5220/0006097300270033
URL : http://arxiv.org/pdf/1612.00220

Z. Lu, M. Shi, and Q. Chen, Crowd counting via scale-adaptive convolutional neural network, WACV, 2018.

L. Zeng, X. Xu, B. Cai, S. Qiu, and T. Zhang, Multi-scale convolutional neural networks for crowd counting, 2017 IEEE International Conference on Image Processing (ICIP), 2017.
DOI : 10.1109/ICIP.2017.8296324
URL : http://arxiv.org/pdf/1702.02359

Z. Wei, Y. Sun, J. Wang, H. Lai, and S. Liu, Learning adaptive receptive fields for deep image parsing network, CVPR, 2017.
DOI : 10.1109/cvpr.2017.420

R. Zhang, S. Tang, Y. Zhang, J. Li, and S. Yan, Scale-Adaptive Convolutions for Scene Parsing, 2017 IEEE International Conference on Computer Vision (ICCV), 2017.
DOI : 10.1109/ICCV.2017.224

E. Parzen, On Estimation of a Probability Density Function and Mode, The annals of mathematical statistics, pp.1065-1076, 1962.
DOI : 10.1214/aoms/1177704472
URL : http://doi.org/10.1214/aoms/1177704472

D. Hoiem, A. A. Efros, and M. Hebert, Putting Objects in Perspective, International Journal of Computer Vision, vol.57, issue.2, pp.3-15, 2008.
DOI : 10.1007/s11263-008-0137-5
URL : http://www.cs.cmu.edu/~dhoiem/publications/ijcv2008ObjectsInPerspective.pdf

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, ICLR, 2015.

H. Noh, S. Hong, and B. Han, Learning Deconvolution Network for Semantic Segmentation, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.178
URL : http://arxiv.org/pdf/1505.04366

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, 2014.
DOI : 10.1145/2647868.2654889