Fetching the paper…
Reading the bibliography…
In this work, we consider the problem of pedestrian detection in natural scenes.
Detecting pedestrians using patterns of motion and appearance
Paul Viola, Michael J Jones, and Daniel Snow · 2003
Earlier work this paper cites.
Robust real-time face detection
Paul Viola and Michael J Jones · 2004
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
Earlier work this paper cites.
Depth and appearance for mobile scene analysis
Andreas Ess, Bastian Leibe, and Luc Van Gool · 2007
Earlier work this paper cites.
Unsupervised learning of invariant feature hierarchies with applications to object recognition
Marc Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau, and Yann LeCun · 2007
Earlier work this paper cites.
Integral channel features
Piotr Dollár, Zhuowen Tu, Pietro Perona, and Serge Belongie · 2009
Earlier work this paper cites.
An hog-lbp human detector with partial occlusion handling
Xiaoyu Wang, Tony X Han, and Shuicheng Yan · 2009
Earlier work this paper cites.
Object detection with discriminatively trained part-based models
Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan · 2010
Earlier work this paper cites.
Multiresolution models for object detection
Dennis Park, Deva Ramanan, and Charless Fowlkes · 2010
Earlier work this paper cites.
Pedestrian detection: An evaluation of the state of the art
Piotr Dollar, Christian Wojek, Bernt Schiele, and Pietro Perona · 2012
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
Earlier work this paper cites.
Discriminative decorrelation for clustering and classification
Bharath Hariharan, Jitendra Malik, and Deva Ramanan · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
A discriminative deep model for pedestrian detection with occlusion handling
Wanli Ouyang and Xiaogang Wang · 2012
Earlier work this paper cites.
Detection evolution with multi-order contextual co-occurrence
Guang Chen, Yuanyuan Ding, Jing Xiao, and Tony X Han · 2013
Earlier work this paper cites.
Joint deep learning for pedestrian detection
Wanli Ouyang and Xiaogang Wang · 2013
Earlier work this paper cites.
Overfeat: Integrated recognition, localization and detection using convolutional networks
Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Rob Fergus, and Yann LeCun · 2013
Cited alongside, same era.
Pedestrian detection with unsupervised multi-stage feature learning
Pierre Sermanet, Koray Kavukcuoglu, Sandhya Chintala, and Yann LeCun · 2013
Cited alongside, same era.
Selective search for object recognition
Jasper RR Uijlings, Koen EA van de Sande, Theo Gevers, and Arnold WM Smeulders · 2013
Cited alongside, same era.
Regionlets for generic object detection
Xiaoyu Wang, Ming Yang, Shenghuo Zhu, and Yuanqing Lin · 2013
Cited alongside, same era.
Robust multi-resolution pedestrian detection in traffic scenes
Junjie Yan, Xucong Zhang, Zhen Lei, Shengcai Liao, and Stan Li · 2013
Cited alongside, same era.
Strip features for fast object detection
Distributed object detection with linear svms
Yanwei Pang, Kun Zhang, Yuan Yuan, and Kongqiao Wang · 2014
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Later among the works it cites.
Scene-specific pedestrian detection for static video surveillance
Xiaogang Wang, Meng Wang, and Wei Li · 2014
Later among the works it cites.
Scale-invariant convolutional neural networks
Yichong Xu, Tianjun Xiao, Jiaxing Zhang, Kuiyuan Yang, and Zheng Zhang · 2014
Later among the works it cites.
Informed haar-like features improve pedestrian detection
Shaoting Zhang, Christian Bauckhage, and Armin Cremers · 2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wei Zheng, Hong Chang, Luhong Liang, Haoyu Ren, Shiguang Shan, and Xilin Chen · 2013
Cited alongside, same era.
Multiscale combinatorial grouping
Pablo Arbelaez, Jordi Pont-Tuset, Jonathan Barron, Ferran Marques, and Jagannath Malik · 2014
Cited alongside, same era.
Ten years of pedestrian detection, what have we learned?
Rodrigo Benenson, Mohamed Omran, Jan Hosang, and Bernt Schiele · 2014
Cited alongside, same era.
Fast feature pyramids for object detection
Piotr Dollár, Ron Appel, Serge Belongie, and Pietro Perona · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jagannath Malik · 2014
Cited alongside, same era.
Multi-scale orderless pooling of deep convolutional activation features
Yunchao Gong, Liwei Wang, Ruiqi Guo, and Svetlana Lazebnik · 2014
Cited alongside, same era.
Spatial pyramid pooling in deep convolutional networks for visual recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2014
Cited alongside, same era.
C Lawrence Zitnick and Piotr Dollár · 2014
Later among the works it cites.
3d object proposals for accurate object class detection
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G Berneshawi, Huimin Ma, Sanja Fidler, and Raquel Urtasun · 2015
Closest in time.
Fast r-cnn
Ross Girshick · 2015
Closest in time.
Taking a deeper look at pedestrians
Jan Hosang, Mohamed Omran, Rodrigo Benenson, and Bernt Schiele · 2015
Closest in time.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Closest in time.
Deep learning strong parts for pedestrian detection
Yonglong Tian, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Closest in time.
Pedestrian detection aided by deep learning semantic tasks
Yonglong Tian, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Closest in time.
Filtered channel features for pedestrian detection
Shanshan Zhang, Rodrigo Benenson, and Bernt Schiele · 2015
Closest in time.
Learning complexity-aware cascades for deep pedestrian detection
Mohammad Saberian Zhaowei Cai and Nuno Vasconcelos · 2015
Closest in time.
Learning sampling distributions for efficient object detection
Y Pang, J Cao, and X Li · 2016
Closest in time.