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Detecting individual pedestrians in a crowd remains a challenging problem since the pedestrians often gather together and occlude each other in real-world scenarios.
Integral channel features
P. Dollár, Z. Tu, P. Perona, and S. Belongie · 2009
Earlier work this paper cites.
Pedestrian detection: A benchmark
P. Dollár, C. Wojek, B. Schiele, and P. Perona · 2009
Earlier work this paper cites.
Integral channel features
P. Dollár, Z. Tu, P. Perona, and S. Belongie · 2009
Earlier work this paper cites.
Pedestrian detection: A benchmark
P. Dollár, C. Wojek, B. Schiele, and P. Perona · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Discriminative models for multi-class object layout
C. Desai, D. Ramanan, and C. C. Fowlkes · 2011
Earlier work this paper cites.
Discriminative models for multi-class object layout
C. Desai, D. Ramanan, and C. C. Fowlkes · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
A discriminative deep model for pedestrian detection with occlusion handling
W. Ouyang and X. Wang · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
A discriminative deep model for pedestrian detection with occlusion handling
W. Ouyang and X. Wang · 2012
Earlier work this paper cites.
Fast feature pyramids for object detection
P. Dollár, R. Appel, S. Belongie, and P. Perona · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Local decorrelation for improved detection
W. Nam, P. Dollár, and J. H. Han · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Fast feature pyramids for object detection
P. Dollár, R. Appel, S. Belongie, and P. Perona · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Local decorrelation for improved detection
W. Nam, P. Dollár, and J. H. Han · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Fast r-cnn
R. Girshick · 2015
Earlier work this paper cites.
Taking a deeper look at pedestrians
J. Hosang, M. Omran, R. Benenson, and B. Schiele · 2015
Earlier work this paper cites.
Densebox: Unifying landmark localization with end to end object detection
L. Huang, Y. Yang, Y. Deng, and Y. Yu · 2015
Earlier work this paper cites.
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.
Convolutional channel features
B. Yang, J. Yan, Z. Lei, and S. Z. Li · 2015
Cited alongside, same era.
Filtered channel features for pedestrian detection
S. Zhang, R. Benenson, and B. Schiele · 2015
Cited alongside, same era.
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
Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
Later among the works it cites.
Unitbox: An advanced object detection network
J. Yu, Y. Jiang, Z. Wang, Z. Cao, and T. Huang · 2016
Later among the works it cites.
Is faster r-cnn doing well for pedestrian detection?
L. Zhang, L. Lin, X. Liang, and K. He · 2016
Later among the works it cites.
How far are we from solving pedestrian detection?
S. Zhang, R. Benenson, M. Omran, J. Hosang, and B. Schiele · 2016
Later among the works it cites.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Closest in time.
Learning non-maximum suppression
J. Hosang, R. Benenson, and B. Schiele · 2017
Closest in time.
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L. Huang, Y. Yang, Y. Deng, and Y. Yu · 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.
Convolutional channel features
B. Yang, J. Yan, Z. Lei, and S. Z. Li · 2015
Cited alongside, same era.
Filtered channel features for pedestrian detection
S. Zhang, R. Benenson, and B. Schiele · 2015
Cited alongside, same era.
A unified multi-scale deep convolutional neural network for fast object detection
Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos · 2016
Cited alongside, same era.
J. Li, X. Liang, S. Shen, T. Xu, J. Feng, and S. Yan · 2017
Closest in time.
Fully convolutional instance-aware semantic segmentation
Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei · 2017
Closest in time.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Closest in time.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Closest in time.
What can help pedestrian detection?
J. Mao, T. Xiao, Y. Jiang, and Z. Cao · 2017
Closest in time.
Citypersons: A diverse dataset for pedestrian detection
S. Zhang, R. Benenson, and B. Schiele · 2017
Closest in time.
Multi-label learning of part detectors for heavily occluded pedestrian detection
C. Zhou and J. Yuan · 2017
Closest in time.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Closest in time.
Learning non-maximum suppression
J. Hosang, R. Benenson, and B. Schiele · 2017
Closest in time.
Scale-aware fast r-cnn for pedestrian detection
J. Li, X. Liang, S. Shen, T. Xu, J. Feng, and S. Yan · 2017
Closest in time.
Fully convolutional instance-aware semantic segmentation
Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei · 2017
Closest in time.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Closest in time.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Closest in time.
What can help pedestrian detection?
J. Mao, T. Xiao, Y. Jiang, and Z. Cao · 2017
Closest in time.
Citypersons: A diverse dataset for pedestrian detection
S. Zhang, R. Benenson, and B. Schiele · 2017
Closest in time.
Multi-label learning of part detectors for heavily occluded pedestrian detection
C. Zhou and J. Yuan · 2017
Closest in time.