caltech行人检测数据集上的论文
地址 ：http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/files/algorithms.pdf
[1] A. Angelova, A. Krizhevsky, V. Vanhoucke
Pedestrian Detection with a Large-Field-Of-View Deep Network
ICRA 2015, Seattle, WA. 1
[2] A. Angelova, A. Krizhevsky, V. Vanhoucke, A. Ogale, and D. Ferguson
Real-Time Pedestrian Detection With Deep Network Cascades
BMVC 2015, Swansea, UK. 1
[3] A. Bar-Hillel, D. Levi, E. Krupka, and C. Goldberg
Part-based Feature Synthesis for Human Detection
ECCV 2010, Crete, Greece. 1
2
[4] R. Benenson, Mathias M., R. Timofte, and L. Van Gool
Pedestrian detection at 100 Frames Per Second
CVPR 2012, Providence, Rhode Island. 1, 2
[5] R. Benenson, M. Mathias, T. Tuytelaars and L. Van Gool
Seeking the strongest rigid detector
CVPR 2013, Portland, OR. 2
[6] R. Benenson, M. Omran, J. Hosang, and B. Schiele
Ten years of pedestrian detection, what have we learned?
ECCV-CVRSUAD 2014, Zurich, Switzerland. 1
[7] G. Brazil, X. Yin, and X. Liu
Illuminating Pedestrians via Simultaneous Detection & Segmentation
ICCV 2017, Venice, Italy. 2
[8] Z. Cai, M. Saberian, and N. Vasconcelos
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
ICCV 2015, Santiago, Chile. 1
[9] Z. Cai, Q. Fan, R. Feris, and N. Vasconcelos
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
ECCV 2016, Amsterdam, The Netherlands. 2
[10] G. Chen, Y. Ding, J. Xiao, and T. Han
Detection Evolution with Multi-order Contextual Co-occurrence.
CVPR 2013, Portland, OR. 2
[11] A. D. Costea and S. Nedevschi
Word Channel Based Multiscale Pedestrian Detection
Without Image Resizing and Using Only One Classifier
CVPR 2014, Columbus, Ohio. 2
[12] A. D. Costea and S. Nedevschi
Semantic Channels for Fast Pedestrian Detection
CVPR 2016, Las Vegas, Nevada. 2
[13] A. D. Costea, A. Vesa, and S. Nedevschi
Fast Pedestrian Detection for Mobile Devices
ITSC 2015, Canary Islands. 1
[14] N. Dalal and B. Triggs
Histogram of Oriented Gradient for Human Detection
CVPR 2005, San Diego, California. 1
[15] P. Doll´ar, R. Appel and W. Kienzle
Crosstalk Cascades for Frame-Rate Pedestrian Detection
ECCV 2012, Florence Italy. 1
[16] P. Doll´ar, S. Belongie and P. Perona
The Fastest Pedestrian Detector in the West
BMVC 2010, Aberystwyth, UK. 1
[17] P. Doll´ar, Z. Tu, H. Tao and S. Belongie
Feature Mining for Image Classification
CVPR 2007, Minneapolis, Minnesota. 1
3
[18] P. Doll´ar, Z. Tu, P. Perona and S. Belongie
Integral Channel Features
BMVC 2009, London, England. 1
[19] P. Doll´ar, R. Appel, S. Belongie, and P. Perona
Fast Feature Pyramids for Object Detection
PAMI, 2014. 1
[20] X. Du, M. El-Khamy, J. Lee, and L. S. Davis
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
arXiv, 2016. 1
[21] X. Du, M. El-Khamy, V. Morariu, J. Lee, and L. S. Davis
Fused Deep Neural Networks for Efficient Pedestrian Detection
arXiv, 2018. 1
[22] P. Felzenszwalb, D. McAllester, D. Ramanan
A Discriminatively Trained, Multiscale, Deformable Part Model
CVPR 2008, Anchorage, Alaska. 1
[23] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan
Object Detection with Discriminatively Trained Part Based Models
PAMI 2010. 1
[24] J. Hosang, M. Omran, R. Benenson, and B. Schiele
Taking a Deeper Look at Pedestrians
CVPR 2015, Boston, Massachusetts. 2
[25] D. Levi, S. Silberstein, A. Bar-Hillel
Fast multiple-part based object detection using KD-Ferns
CVPR 2013, Portland, OR. 1
[26] J. Li, X. Liang, S. Shen, T. Xu, and S. Yan
Scale-aware Fast R-CNN for Pedestrian Detection
arXiv, 2016. 2
[27] J. Lim, C. Lawrence Zitnick, P. Doll´ar
Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection
CVPR 2013, Portland, OR. 2
[28] Z. Lin and L. Davis
A Pose-Invariant Descriptor for Human Detection and Segmentation
ECCV 2008, Marseille, France. 2
[29] P. Luo, Y. Tian, X. Wang, and X. Tang
Switchable Deep Network for Pedestrian Detection
CVPR 2014, Columbus, Ohio. 2
[30] S. Maji, A. C. Berg, J. Malik
Classification Using Intersection Kernel Support Vector Machines is efficient
CVPR 2008, Anchorage, Alaska. 1
[31] J. Marin, D. Vazquez, A. Lopez, J. Amores, B. Leibe
Random Forests of Local Experts for Pedestrian Detection
ICCV 2013, Sydney, Australia. 2
4
[32] M. Mathias, R. Benenson, R. Timofte, L. Van Gool
Handling Occlusions with Franken-classifiers
ICCV 2013, Sydney, Australia. 1
[33] W. Nam, B. Han, and J. H. Han
Improving Object Localization Using Macrofeature Layout Selection
ICCV Workshop on Visual Surveillance 2011, Barcelona, Spain. 2
[34] W. Nam, P. Doll´ar, and J. H. Han
Local Decorrelation For Improved Pedestrian Detection
NIPS 2014, Montreal, Quebec. 1
[35] E. Ohn-Bar and M. Trivedi
To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection
ICPR 2016, Cancun, Mexico. 1
[36] W. Ouyang and X. Wang
A Discriminative Deep Model for Pedestrian Detection with Occlusion Handling
CVPR 2012, Providence, RI. 1
[37] W. Ouyang and X. Wang
Joint Deep Learning for Pedestrian Detection
ICCV 2013, Sydney, Australia. 1
[38] W. Ouyang and X. Wang
Single-pedestrian detection aided by multi-pedestrian detection.
CVPR 2013, Portland, OR. 1, 2
[39] W. Ouyang, X. Zeng and X. Wang
Modeling Mutual Visibility Relationship with a Deep Model in Pedestrian Detection
CVPR 2013, Portland, OR. 1
[40] W. Ouyang, H. Zhou, H. Li, Q. Li, J. Yan and X. Wang
Jointly learning deep features, deformable parts, occlusion
and classification for pedestrian detection
PAMI, 2017. 2
[41] S. Paisitkriangkrai, C. Shen, A. van den Hengel
Efficient pedestrian detection by directly optimize the partial area under the ROC curve
ICCV 2013, Sydney, Australia. 2
[42] S. Paisitkriangkrai, C. Shen, A. van den Hengel
Strengthening the Effectiveness of Pedestrian Detection
ECCV 2014, Zurich, Switzerland. 2
[43] S. Paisitkriangkrai, C. Shen, A. van den Hengel
Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning
arXiv, 2014. 2
[44] D. Park, D. Ramanan, C. Fowlkes
Multiresolution models for object detection
ECCV 2010, Crete, Greece. 2
[45] D. Park, C. Lawrence Zitnick, D. Ramanan, P. Doll´ar
Exploring Weak Stabilization for Motion Feature Extraction
CVPR 2013, Portland, OR. 1
5
[46] P. Sabzmeydani and G. Mori
Detecting pedestrians by learning shapelet features
CVPR 2007, Minneapolis, Minnesota. 2
[47] W.R. Schwartz, A. Kembhavi, D. Harwood, L. S. Davis
Human Detection Using Partial Least Squares Analysis
ICCV 2009, Kyoto, Japan. 2
[48] P. Sermanet, K. Kavukcuoglu, S. Chintala, Y. LeCun
Pedestrian Detection with Unsupervised Multi-Stage Feature Learning
CVPR 2013, Portland, OR. 1
[49] C. Shen, P. Wang, S. Paisitkriangkrai, A. van den Hengel
Training Effective Node Classifiers for Cascade Classification
IJCV 2013. 1
[50] T. Song, L. Sun, D. Xie, H. Sun, S. Pu
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature
Aggregation
ECCV 2018, Munich, Germany. 2
[51] Y. Tian, P. Luo, X. Wang, and X. Tang
Pedestrian Detection aided by Deep Learning Semantic Tasks
CVPR 2015, Boston, Massachusetts. 2
[52] Y. Tian, P. Luo, X. Wang, and X. Tang
Deep Learning Strong Parts for Pedestrian Detection
ICCV 2015, Santiago, Chile. 1
[53] C. Toca, M. Ciuc, and C. Patrascu
Normalized Autobinomial Markov Channels For Pedestrian Detection
BMVC 2015, Swansea, UK. 2
[54] P. Viola and M. Jones
Robust Real-Time Face Detection
IJCV 2004. 2
[55] S. Walk, N. Majer, K. Schindler, B. Schiele
New Features and Insights for Pedestrian Detection
CVPR 2010, San Francisco, California. 2
[56] S. Wang, J. Cheng, H. Liu, and M. Tang
PCN: Part and context information for pedestrian detection with CNNs
BMVC 2017, London, UK. 2
[57] X. Wang, T. X. Han, and S. Yan
An HOG-LBP Human Detector with Partial Occlusion Handling
ICCV 2009, Kyoto, Japan. 1
[58] C. Wojek and B. Schiele
A Performance Evaluation of Single and Multi-Feature People Detection
DAGM 2008, Munich, Germany. 2
[59] J. Yan, X. Zhang, Z. Lei, S. Liao, S. Z. Li
Robust Multi-Resolution Pedestrian Detection in Traffic Scenes
CVPR 2013, Portland, OR. 2
6
[60] B. Yang, J. Yan, Z. Lei, and S. Z. Li
Convolutional Channel Features
ICCV 2015, Santiago, Chile. 1
[61] Y. Yang, Z. Wang, and F. Wu
Exploring Prior Knowledge for Pedestrian Detection
BMVC 2015, Swansea, UK. 2
[62] X. Zeng, W. Ouyang, X. Wang
Multi-Stage Contextual Deep Learning for Pedestrian Detection
ICCV 2013, Sydney, Australia. 2
[63] L. Zhang, L. Lin, X. Liang, K. He
Is Faster R-CNN Doing Well for Pedestrian Detection?
ECCV 2016, Amsterdam, The Netherlands. 2
[64] S. Zhang, C. Bauckhage, and A. B. Cremers
Informed Haar-like Features Improve Pedestrian Detection
CVPR 2014, Columbus, Ohio. 1
[65] S. Zhang, R. Benenson, and B. Schiele
Filtered channel features for pedestrian detection
CVPR 2015, Boston, Massachusetts. 1
[66] S. Zhang, R. Benenson, and B. Schiele
CityPersons: A Diverse Dataset for Pedestrian Detection
CVPR 2017, Honolulu, Hawaii. 1
[67] S. Zhang, J. Yang, and B. Schiele
Occluded Pedestrian Detection Through Guided Attention in CNNs
CVPR 2018, Salt Lake City, Utah. 1