.. .. Related-work,

. .. Performance-characteristics, 3.2 Concatenation of pedestrian probability map and deformable convolution output

. .. , Two-Step classification and regression, p.107

.. .. Input-size,

. .. Proposed-approach,

. Experiments, . Results, and . .. Analysis,

, 7.2 Impact of anchor selection layer

.. .. Conclusions,

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis et al., Tensorflow: A system for large-scale machine learning, 12th {USENIX} Symposium on Operating Systems Design and Implementation, p.95, 2016.

A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-fei et al., Social lstm: Human trajectory prediction in crowded spaces, Proceedings of the IEEE conference on computer vision and pattern recognition, vol.120, pp.961-971, 2016.

B. Alexe, T. Deselaers, and V. Ferrari, Measuring the objectness of image windows, vol.34, pp.2189-2202, 2012.

A. Ali and E. Dagless, Vehicle and pedestrian detection and tracking, IEE Colloquium on Image Analysis for Transport Applications, pp.5-6, 1990.

A. Angelova, A. Krizhevsky, V. Vanhoucke, A. Ogale, and D. Ferguson, Real-time pedestrian detection with deep network cascades, p.16

J. Baek, J. Hyun, and E. Kim, A pedestrian detection system accelerated by kernelized proposals, IEEE Transactions on Intelligent Transportation Systems, p.16, 2019.

S. Bai, Growing random forest on deep convolutional neural networks for scene categorization, Expert Systems with Applications, vol.71, p.57, 2017.

S. Bell, C. Lawrence-zitnick, K. Bala, and R. Girshick, Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks, Proceedings of the IEEE conference on computer vision and pattern recognition, p.27, 2016.

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, vol.83, pp.2903-2910, 2012.

R. Benenson, M. Omran, J. Hosang, and B. Schiele, Ten years of pedestrian detection, what have we learned, European Conference on Computer Vision, p.39, 2014.

R. D. Blomberg, W. A. Leaf, and H. H. Jacobs, Detection and recognition of pedestrians at night, Transportation Research Circular, vol.229, pp.17-21, 1981.

N. Bodla, B. Singh, R. Chellappa, and L. S. Davis, Soft-nmsâimproving object detection with one line of code, Computer Vision (ICCV), 2017 IEEE International Conference on, p.58, 2017.

M. Braun, S. Krebs, F. Flohr, and D. M. Gavrila, The eurocity persons dataset: A novel benchmark for object detection, vol.120, 2018.

G. Brazil, X. Yin, and X. Liu, Illuminating pedestrians via simultaneous detection & segmentation, Proceedings of the IEEE International Conference on Computer Vision, vol.112, pp.4950-4959, 2017.

Z. Cai, Q. Fan, R. Feris, and N. Vasconcelos, A unified multi-scale deep convolutional neural network for fast object detection, ECCV, vol.50, 2016.

Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos, A unified multi-scale deep convolutional neural network for fast object detection, European conference on computer vision, vol.108, pp.354-370, 2016.

Z. Cai, M. Saberian, and N. Vasconcelos, Learning complexity-aware cascades for deep pedestrian detection, Proceedings of the IEEE International Conference on Computer Vision, vol.83, pp.3361-3369, 2015.

Z. Cai and N. Vasconcelos, Cascade r-cnn: Delving into high quality object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.6154-6162, 2018.

A. Canziani, A. Paszke, and E. Culurciello, An analysis of deep neural network models for practical applications, vol.47, 2016.

J. Cao, Y. Pang, and X. Li, Learning multilayer channel features for pedestrian detection, IEEE transactions on image processing, vol.26, pp.3210-3220, 2017.

L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille et al., Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, vol.40, pp.834-848, 2018.

L. Chen, G. Papandreou, F. Schroff, and H. Adam, Rethinking atrous convolution for semantic image segmentation, vol.90, 2017.

X. Chen and A. Gupta, An implementation of faster rcnn with study for region sampling, 2017.

M. Cheng, Z. Zhang, W. Lin, P. Torr, and . Bing, Binarized normed gradients for objectness estimation at 300fps, Proceedings of the IEEE conference on computer vision and pattern recognition, p.16, 2014.

F. Chollet, Xception: Deep learning with depthwise separable convolutions, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1251-1258, 2017.

S. A. Cohen and D. Hopkins, Autonomous vehicles and the future of urban tourism, Annals of tourism research, vol.74, pp.33-42, 2019.

T. S. Combs, L. S. Sandt, M. P. Clamann, and N. C. Mcdonald, Automated vehicles and pedestrian safety: exploring the promise and limits of pedestrian detection, American journal of preventive medicine, vol.56, pp.1-7, 2019.

M. Cordts, M. Omran, S. Ramos, T. Scharwächter, M. Enzweiler et al., The cityscapes dataset, CVPR Workshop on the Future of Datasets in Vision, vol.1, p.33, 2015.

J. Dai, Y. Li, K. He, J. Sun, and . R-fcn, Object detection via region-based fully convolutional networks, Advances in neural information processing systems, p.102, 2016.

J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang et al., Deformable convolutional networks, Proceedings of the IEEE international conference on computer vision, p.88, 2017.

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, international Conference on computer vision & Pattern Recognition (CVPR'05) (2005), vol.1, pp.886-893
URL : https://hal.archives-ouvertes.fr/inria-00548512

S. K. Divvala, D. Hoiem, J. H. Hays, A. A. Efros, and M. Hebert, An empirical study of context in object detection, 2009 IEEE Conference on computer vision and Pattern Recognition, p.83, 2009.

P. Dollár, C. Wojek, B. Schiele, and P. Perona, Pedestrian detection: A benchmark, vol.38

P. E. Dollar, Pedestrian detection: An evaluation of the state of the art. IEEE transactions on pattern analysis and machine intelligence, 2012.

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang et al., Decaf: A deep convolutional activation feature for generic visual recognition, International conference on machine learning, p.62, 2014.

X. E. Du, Fused dnn: A deep neural network fusion approach to fast and robust pedestrian detection, WACV, vol.77, p.71, 2017.

M. Enzweiler and D. M. Gavrila, Monocular pedestrian detection: Survey and experiments, vol.31, p.39, 2009.

F. Esposito, D. Malerba, G. Semeraro, and J. Kay, A comparative analysis of methods for pruning decision trees, IEEE transactions, vol.19, p.57, 1997.

A. Ess, B. Leibe, and L. Van-gool, Depth and appearance for mobile scene analysis. In Computer Vision, IEEE 11th International Conference on, p.32, 2007.

. Et and B. Al, Learning object motion patterns for anomaly detection and improved object detection, Computer Vision and Pattern Recognition, p.62, 2008.

. Et and B. Al, Improving object detection with one line of code, p.66, 2017.

. Et and E. Al, Improving small object proposals for company logo detection, Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, p.68, 2017.

. Et and K. H. Al, Mask r-cnn, IEEE International Conference on Computer Vision (ICCV, vol.80, 2017.

. Et and L. Al, Feature pyramid networks for object detection, CVPR, vol.28, 2017.

. Et, S. R. Al, and . Faster-r-cnn, Towards real-time object detection with region proposal networks, NIPS, 2015.

. Et and S. Z. Al, How far are we from solving pedestrian detection, CVPR (2016). (Cited on pages xvi, vol.71

. Et and Z. Al, How far are we from solving pedestrian detection?, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.

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.88, pp.303-338, 2010.

L. Fang, X. Zhao, and S. Zhang, Small-objectness sensitive detection based on shifted single shot detector, Multimedia Tools and Applications, p.77, 2018.

P. F. Felzenszwalb, D. A. Mcallester, D. Ramanan, and . Et-al, A discriminatively trained, multiscale, deformable part model, Cvpr, vol.2, p.7, 2008.

Y. Freund, R. E. Schapire, and . Al, Experiments with a new boosting algorithm, icml, vol.96, p.31, 1996.

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, Vision meets robotics: The kitti dataset, The International Journal of Robotics Research, vol.32, pp.1231-1237, 2013.

A. Geiger, P. Lenz, and R. Urtasun, Are we ready for autonomous driving? the kitti vision benchmark suite, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, p.32, 2012.

D. Geronimo, A. M. Lopez, A. D. Sappa, and T. Graf, Survey of pedestrian detection for advanced driver assistance systems, IEEE transactions, vol.32, p.39, 2010.

R. Girshick, Fast r-cnn, Proceedings of the IEEE international conference on computer vision, vol.53, pp.1440-1448, 2015.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, p.102, 2014.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the thirteenth international conference on artificial intelligence and statistics, vol.42, pp.249-256, 2010.

Y. Gong, L. Wang, R. Guo, and S. Lazebnik, Multi-scale orderless pooling of deep convolutional activation features, European conference on computer vision, p.27, 2014.

E. L. Groshen, S. Helper, J. P. Macduffie, and C. Carson, Preparing us workers and employers for an autonomous vehicle future

K. Hara, H. Kataoka, and Y. Satoh, Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.43, 2018.

M. Hardt, B. Recht, and Y. Singer, Train faster, generalize better: Stability of stochastic gradient descent, 2015.

R. K. Harman and J. W. Patchell, Perimeter surveillance system, US Patent, vol.4, p.207, 31981-02.

K. He, G. Gkioxari, P. Dollár, and R. Girshick, Mask r-cnn, Proceedings of the IEEE international conference on computer vision, pp.2961-2969, 2017.

K. He and J. Sun, Convolutional neural networks at constrained time cost, Proceedings of the IEEE conference on computer vision and pattern recognition, p.104, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE transactions, vol.37, p.27, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, vol.44, p.43, 2016.

K. He, X. Zhang, S. Ren, and J. Sun, Identity mappings in deep residual networks, European conference on computer vision, vol.87, pp.630-645, 2016.

G. E. Hinton, A practical guide to training restricted boltzmann machines, Neural networks: Tricks of the trade, p.24, 2012.

M. Holschneider, P. Tchamitchian, R. Kronland-martinet, and J. Morlet, , vol.118, 1988.

J. H. Hosang, R. Benenson, and B. Schiele, Learning non-maximum suppression, CVPR (2017), p.58

J. Hu, L. Shen, and G. Sun, Squeeze-and-excitation networks, Proceedings of the IEEE conference on computer vision and pattern recognition, p.26, 2018.

Q. Hu, P. Wang, C. Shen, . Van-den, A. Hengel et al., Pushing the limits of deep cnns for pedestrian detection, Technology, vol.28, p.31, 2017.

G. Huang, Z. Liu, L. Van-der-maaten, and K. Q. Weinberger, Densely connected convolutional networks, CVPR (2017), vol.1, p.3

J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara et al., Speed/accuracy trade-offs for modern convolutional object detectors, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.7310-7311, 2017.

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, vol.47, 2015.

M. Izadyyazdanabadi, E. Belykh, M. Mooney, N. Martirosyan, J. Es-chbacher et al., Convolutional neural networks: ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical cle images, Journal of Visual Communication and Image Representation, vol.54, pp.10-20, 2018.

B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang et al., Quantization and training of neural networks for efficient integer-arithmetic-only inference, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.76, 2018.

N. Jacobstein, Autonomous vehicles: An imperfect path to saving millions of lives, 2019.

R. Johnson and T. Zhang, Accelerating stochastic gradient descent using predictive variance reduction, Advances in neural information processing systems, vol.40, pp.315-323, 2013.

N. S. Keskar and R. Socher, Improving generalization performance by switching from adam to sgd, 2017.

D. P. Kingma, J. Ba, and . Adam, A method for stochastic optimization, 2014.

M. B. Kjaergaard, M. Wirz, D. Roggen, and G. Tröster, Mobile sensing of pedestrian flocks in indoor environments using wifi signals, 2012 IEEE International Conference on Pervasive Computing and Communications, p.77, 2012.

T. Kong, A. Yao, Y. Chen, and F. Sun, Hypernet: Towards accurate region proposal generation and joint object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, vol.102, pp.845-853, 2016.

P. Kontschieder, M. Fiterau, A. Criminisi, and S. Bulo, Deep neural decision forests, Proceedings of the IEEE international conference on computer vision, vol.57, pp.1467-1475, 2015.

S. Kornblith, J. Shlens, and Q. V. Le, Do better imagenet models transfer better?, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2661-2671, 2019.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.

M. C. Kruithof, H. Bouma, N. M. Fischer, and K. Schutte, Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII, vol.9995, p.99950, 2016.

I. Lahouli, E. Karakasis, R. Haelterman, Z. Chtourou, G. De-cubber et al., Hot spot method for pedestrian detection using saliency maps, discrete chebyshev moments and support vector machine, IET Image Processing, vol.12, p.16, 2018.

T. A. Lampert, A. Stumpf, and P. Gançarski, An empirical study into annotator agreement, ground truth estimation, and algorithm evaluation, IEEE Transactions on Image Processing, vol.25, p.121, 2016.

W. Lan, J. Dang, Y. Wang, and S. Wang, Pedestrian detection based on yolo network model, 2018 IEEE International Conference on Mechatronics and Automation (ICMA), vol.78, pp.1547-1551, 2018.

Y. Lecun and C. Cortes, MNIST handwritten digit database, p.24

H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, Proceedings of the 26th annual international conference on machine learning, vol.63, pp.609-616, 2009.

B. Li, Q. Yao, and K. Wang, A review on vision-based pedestrian detection in intelligent transportation systems, Networking, Sensing and Control (ICNSC), p.39, 2012.

J. Li, X. Liang, S. Shen, T. Xu, J. Feng et al., Scale-aware fast r-cnn for pedestrian detection, IEEE transactions on Multimedia, vol.20, pp.985-996, 2017.

J. Li, X. Liang, S. Shen, T. Xu, J. Feng et al., Scale-aware fast r-cnn for pedestrian detection, IEEE transactions on Multimedia, vol.20, pp.985-996, 2018.

D. Lin, S. Talathi, and S. Annapureddy, Fixed point quantization of deep convolutional networks, International Conference on Machine Learning, p.76, 2016.

T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, Focal loss for dense object detection, Proceedings of the IEEE international conference on computer vision, p.27, 2017.

T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, Focal loss for dense object detection, IEEE transactions on pattern analysis and machine intelligence, vol.53, 2018.

T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona et al., Microsoft coco: Common objects in context, European conference on computer vision, pp.740-755, 2014.

J. Liu, S. Zhang, S. Wang, and D. N. Metaxas, Multispectral deep neural networks for pedestrian detection, p.16, 2016.

M. Liu, J. Shi, Z. Li, C. Li, J. Zhu et al., Towards better analysis of deep convolutional neural networks, IEEE transactions on visualization and computer graphics, vol.23, p.26, 2016.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed et al., Single shot multibox detector, European conference on computer vision, vol.6, pp.21-37, 1997.

W. Liu, S. Liao, W. Hu, X. Liang, and X. Chen, Learning efficient singlestage pedestrian detectors by asymptotic localization fitting, Proceedings of the European Conference on Computer Vision (ECCV), pp.618-634, 2018.

W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu et al., A survey of deep neural network architectures and their applications, Neurocomputing, vol.234, p.26, 2017.

P. Luo, Y. Tian, X. Wang, and X. Tang, Switchable deep network for pedestrian detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.899-906, 2014.

P. Luo, X. Wang, and X. Tang, Pedestrian parsing via deep decompositional network, Proceedings of the IEEE international conference on computer vision, p.24, 2013.

S. Manen, M. Guillaumin, and L. Van-gool, Prime object proposals with randomized prim's algorithm, Proceedings of the IEEE international conference on computer vision, p.16, 2013.

J. Mao, T. Xiao, Y. Jiang, and Z. Cao, What can help pedestrian detection?, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3127-3136, 2017.

V. Molchanov, B. Vishnyakov, Y. Vizilter, O. Vishnyakova, and V. Knyaz, Pedestrian detection in video surveillance using fully convolutional yolo neural network, Automated Visual Inspection and Machine Vision II, vol.10334, p.103340, 2017.

T. K. Moon and W. C. Stirling, Mathematical methods and algorithms for signal processing, vol.1, 2000.

H. Mori, N. M. Charkari, and T. Matsushita, On-line vehicle and pedestrian detections based on sign pattern, IEEE Transactions on industrial electronics, vol.41, pp.384-391, 1994.

L. Neumann, M. Karg, S. Zhang, C. Scharfenberger, E. Piegert et al., Nightowls: A pedestrians at night dataset, vol.120

L. Neumann, A. Zisserman, and A. Vedaldi, Relaxed softmax: Efficient confidence auto-calibration for safe pedestrian detection, p.77

J. Noh, S. Lee, B. Kim, and G. Kim, Improving occlusion and hard negative handling for single-stage pedestrian detectors, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

S. Oh, A. Hoogs, A. Perera, N. Cuntoor, C. Chen et al., A large-scale benchmark dataset for event recognition in surveillance video, CVPR 2011, vol.120, pp.3153-3160, 2011.

A. Oliva, Gist of the scene, Neurobiology of attention, vol.83, pp.251-256, 2005.

M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio, Pedestrian detection using wavelet templates, cvpr, vol.97, p.16, 1997.

W. Ouyang and X. Wang, Joint deep learning for pedestrian detection, Proceedings of the IEEE International Conference on Computer Vision, pp.2056-2063, 2013.

W. Ouyang, H. Zhou, H. Li, Q. Li, J. Yan et al., Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection, vol.40, p.78, 2018.

Q. Peng, W. Luo, G. Hong, M. Feng, Y. Xia et al., Pedestrian detection for transformer substation based on gaussian mixture model and yolo, 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (2016), vol.2, pp.562-565, 2016.

R. D. Peterson, Vehicle mounted surveillance system, 1990.

M. Raghu, B. Poole, J. Kleinberg, S. Ganguli, and J. Sohl-dickstein, On the expressive power of deep neural networks, vol.56, 2016.

E. Rahtu, J. Kannala, and M. Blaschko, Learning a category independent object detection cascade, 2011 international conference on Computer Vision, p.16, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00855735

Y. Ran, I. Weiss, Q. Zheng, and L. S. Davis, Pedestrian detection via periodic motion analysis, International Journal of Computer Vision, vol.71, p.16, 2007.

I. Reading, C. Wan, and K. Dickinson, Developments in pedestrian detection, Traffic engineering and control, vol.36, pp.538-542, 1995.

S. J. Reddi, S. Kale, and S. Kumar, On the convergence of adam and beyond, vol.40

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: Unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.779-788, 2016.

J. Redmon and A. Farhadi, Yolo9000: better, faster, stronger. arXiv preprint, 2017.

J. Ren, X. Chen, J. Liu, W. Sun, J. Pang et al., Accurate single stage detector using recurrent rolling convolution, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol.78, pp.5420-5428, 2017.

S. Ren, K. He, R. Girshick, and J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in neural information processing systems, vol.16, pp.91-99, 2015.

A. Robicquet, A. Sadeghian, A. Alahi, and S. Savarese, Learning social etiquette: Human trajectory understanding in crowded scenes, European conference on computer vision, vol.120, pp.549-565, 2016.

J. Schmidhuber and S. Hochreiter, Long short-term memory, Neural Comput, vol.9, pp.1735-1780, 1997.

D. Scieur, A. D'aspremont, and F. Bach, Regularized nonlinear acceleration, Advances In Neural Information Processing Systems, vol.40, pp.712-720, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01384682

D. Scieur, E. Oyallon, A. D'aspremont, and F. Bach, Nonlinear acceleration of deep neural networks, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01799269

P. E. Sermanet, Pedestrian detection with unsupervised multi-stage feature learning, CVPR, 2013.

A. Sharif-razavian, H. Azizpour, J. Sullivan, and S. Carlsson, Cnn features off-the-shelf: an astounding baseline for recognition, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp.806-813, 2014.

A. Shrivastava, A. Gupta, and R. Girshick, Training region-based object detectors with online hard example mining, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol.11, pp.761-769, 2016.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre et al., Mastering the game of go with deep neural networks and tree search, nature, vol.529, p.57, 2016.

E. Simo-serra, E. Trulls, L. Ferraz, I. Kokkinos, and F. Moreno-noguer, , vol.44, 2014.

K. Simonyan and A. Zisserman, Very deep convolutional networks for largescale image recognition, vol.26, 2014.

K. Simonyan and A. Zisserman, Very deep convolutional networks for largescale image recognition, vol.55, 2014.

B. Singh and L. S. Davis, An analysis of scale invariance in object detection snip, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.3578-3587, 2018.

K. Sung, Learning and example selection for object and pattern detection, vol.10

V. Sze, Y. Chen, T. Yang, and J. S. Emer, Efficient processing of deep neural networks: A tutorial and survey, Proceedings of the IEEE 105, vol.12, pp.2295-2329, 2017.

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, Inception-v4, inceptionresnet and the impact of residual connections on learning, Thirty-First AAAI Conference on Artificial Intelligence, p.47, 2017.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions, Proceedings of the IEEE conference on computer vision and pattern recognition, p.43, 2015.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.2818-2826, 2016.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan et al., Intriguing properties of neural networks, p.26, 2013.

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall et al., Convolutional neural networks for medical image analysis: Full training or fine tuning?, IEEE transactions on medical imaging, vol.35, pp.1299-1312, 2016.

S. Tang, M. Andriluka, and B. Schiele, Detection and tracking of occluded people, International Journal of Computer Vision, vol.110, pp.58-69, 2014.

C. Thorpe, M. Herbert, T. Kanade, and S. Shafer, Toward autonomous driving: the cmu navlab. i. perception, IEEE expert, vol.6, pp.31-42, 1991.

Y. Tian, P. Luo, X. Wang, and X. Tang, Pedestrian detection aided by deep learning semantic tasks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.16, 2015.

Y. E. Tian, Deep learning strong parts for pedestrian detection, CVPR, 2015.

T. Tommasi, N. Patricia, B. Caputo, and T. Tuytelaars, A deeper look at dataset bias, Domain Adaptation in Computer Vision Applications, pp.37-55, 2017.

L. Tran, X. Yin, and X. Liu, Disentangled representation learning gan for poseinvariant face recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1415-1424, 2017.

T. Tsukiyama and Y. Shirai, Detection of the movements of persons from a sparse sequence of tv images, Pattern Recognition, vol.18, pp.207-213, 1985.

J. R. Uijlings, K. E. Van-de-sande, T. Gevers, and A. W. Smeulders, Selective search for object recognition, International journal of computer vision, vol.104, pp.154-171, 2013.

D. Ujjwal, A. Leroy, B. Bremond, and F. , Late fusion of multiple convolutional layers for pedestrian detection, International Conference on Advanced Video and Signal Based Surveillance, vol.56, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01926073

D. Ujjwal, L. B. Aziz, and B. Francois, Late fusion of multiple convolutional layers for pedestrian detection
URL : https://hal.archives-ouvertes.fr/hal-01926073

A. Veit, M. J. Wilber, and S. Belongie, Residual networks behave like ensembles of relatively shallow networks, Advances in Neural Information Processing Systems, p.45, 2016.

P. Viola and M. J. Jones, Robust real-time face detection, International journal of computer vision, vol.57, p.58, 2004.

J. Walker, A. Gupta, and M. Hebert, Patch to the future: Unsupervised visual prediction, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, vol.120, pp.3302-3309, 2014.

B. C. Wallace, K. Small, C. E. Brodley, and T. A. Trikalinos, Class imbalance, redux, IEEE 11th international conference on data mining, vol.10, pp.754-763, 2011.

X. Wang, X. Bai, W. Liu, and L. J. Latecki, Feature context for image classification and object detection, CVPR 2011, vol.83, pp.961-968, 2011.

X. Wang and W. Ouyang, A discriminative deep model for pedestrian detection with occlusion handling, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3258-3265, 2012.

X. Wang, T. Xiao, Y. Jiang, S. Shao, J. Sun et al., Repulsion loss: Detecting pedestrians in a crowd, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.7774-7783, 2018.

A. C. Wilson, R. Roelofs, M. Stern, N. Srebro, and B. Recht, The marginal value of adaptive gradient methods in machine learning, Advances in Neural Information Processing Systems, vol.40, pp.4148-4158, 2017.

C. Wojek, S. Walk, and B. Schiele, Multi-cue onboard pedestrian detection, 2009 IEEE Conference on Computer Vision and Pattern Recognition, vol.32, pp.794-801, 2009.

Y. Xu, T. Xiao, J. Zhang, K. Yang, and Z. Zhang, Scale-invariant convolutional neural networks, p.27, 2014.

J. Xue, J. Fang, and P. Zhang, A survey of scene understanding by event reasoning in autonomous driving, International Journal of Automation and Computing, vol.15, p.119, 2018.

F. Yang, H. Chen, J. Li, F. Li, L. Wang et al., Single shot multibox detector with kalman filter for online pedestrian detection in video, IEEE Access, p.77, 2019.

T. Yang, X. Zhang, Z. Li, W. Zhang, J. Sun et al., Learning to detect objects with customized anchors, Advances in Neural Information Processing Systems, p.41, 2018.

S. Yasutomi and H. Mori, A method for discriminating of pedestrian based on rhythm, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94), vol.2, pp.988-995, 1994.

F. Yu, W. Xian, Y. Chen, F. Liu, M. Liao et al., A diverse driving video database with scalable annotation tooling, 2018.

M. D. Zeiler, Adadelta: an adaptive learning rate method, 2012.

M. D. Zeiler and R. Fergus, Visualizing and understanding convolutional networks, European conference on computer vision, pp.818-833, 2014.

L. Zhang, L. Lin, X. Liang, and K. He, Is faster r-cnn doing well for pedestrian detection?, European conference on computer vision, vol.112, pp.443-457, 1996.

S. Zhang, R. Benenson, M. Omran, J. Hosang, and B. Schiele, Towards reaching human performance in pedestrian detection, IEEE transactions, vol.40, pp.973-986, 2018.

S. Zhang, R. Benenson, and B. Schiele, Citypersons: A diverse dataset for pedestrian detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol.108, pp.3213-3221, 2017.

S. Zhang, R. Benenson, B. Schiele, and . Et-al, Filtered channel features for pedestrian detection, CVPR (2015), vol.1, p.4

S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li, Occlusion-aware r-cnn: detecting pedestrians in a crowd, Proceedings of the European Conference on Computer Vision (ECCV), p.77, 2018.

S. Zhang, J. Yang, and B. Schiele, Occluded pedestrian detection through guided attention in cnns, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p.103, 2018.

X. Zhang, L. Cheng, B. Li, and H. Hu, Too far to see? not really!âpedestrian detection with scale-aware localization policy, IEEE transactions on image processing, vol.27, pp.3703-3715, 2018.

Z. Zhou, J. Feng, and . Forest, Towards an alternative to deep neural networks, vol.57, 2017.

C. L. Zitnick and P. Dollár, Edge boxes: Locating object proposals from edges, European conference on computer vision, vol.17, pp.391-405, 2014.

Y. Zuo and T. Drummond, Fast residual forests: Rapid ensemble learning for semantic segmentation, Conference on Robot Learning (2017), p.57