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Convnets have enabled significant progress in pedestrian detection recently, but there are still open questions regarding suitable architectures and training data.
Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
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A critical view of context
L. Wolf and S. M. Bileschi · 2006
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A mobile vision system for robust multi-person tracking
A. Ess, B. Leibe, K. Schindler, and L. Van Gool · 2008
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Monocular pedestrian detection: Survey and experiments
M. Enzweiler and D. M. Gavrila · 2009
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Multi-cue onboard pedestrian detection
C. Wojek, S. Walk, and B. Schiele · 2009
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Pedestrian detection: An evaluation of the state of the art
P. Dollár, C. Wojek, B. Schiele, and P. Perona · 2012
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Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Ten years of pedestrian detection, what have we learned?
R. Benenson, M. Omran, J. Hosang, , and B. Schiele · 2014
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Fast feature pyramids for object detection
P. Dollár, R. Appel, S. Belongie, and P. Perona · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Real-time pedestrian detection with deep network cascades
A. Angelova, A. Krizhevsky, V. Vanhoucke, A. Ogale, and D. Ferguson · 2015
Cited alongside, same era.
Learning complexity-aware cascades for deep pedestrian detection
Z. Cai, M. Saberian, and N. Vasconcelos · 2015
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Fast r-cnn
R. Girshick · 2015
Cited alongside, same era.
Taking a deeper look at pedestrians
J. Hosang, M. Omran, R. Benenson, and B. Schiele · 2015
Cited alongside, same era.
Densebox: Unifying landmark localization with end to end object detection
L. Huang, Y. Yang, Y. Deng, and Y. Yu · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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Filtered channel features for pedestrian detection
S. Zhang, R. Benenson, and B. Schiele · 2015
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A unified multi-scale deep convolutional neural network for fast object detection
Z. Cai, Q. Fan, R. Feris, and N. Vasconcelos · 2016
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Semantic channels for fast pedestrian detection
A. D. Costea and S. Nedevschi · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Pushing the limits of deep cnns for pedestrian detection
Q. Hu, P. Wang, C. Shen, A. van den Hengel, and F. Porikli · 2016
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W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2015
Cited alongside, same era.
Fully convolutional models for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Deep learning strong parts for pedestrian detection
Y. Tian, P. Luo, X. Wang, and X. Tang · 2015
Cited alongside, same era.
Pedestrian detection aided by deep learning semantic tasks
Y. Tian, P. Luo, X. Wang, and X. Tang · 2015
Cited alongside, same era.
Scale-aware fast r-cnn for pedestrian detection
J. Li, X. Liang, S. Shen, T. Xu, and S. Yan · 2016
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Amodal instance segmentation
K. Li and J. Malik · 2016
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You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
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Is faster r-cnn doing well for pedestrian detection?
L. Zhang, L. Lin, X. Liang, and K. He · 2016
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How far are we from solving pedestrian detection?
S. Zhang, R. Benenson, M. Omran, J. Hosang, and B. Schiele · 2016
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