行人检测论文大全

行人检测论文汇总

马上就研二了，压力山大，回想研一整个一年，忙忙碌碌，却没出什么成绩，真是。。回首研一，可能就做了一点点工资，学习一些基本知识，比如常用机器学习算法、opencv、图像处理等等，然后开始接触行人检测，读dollar大神的论文，读他的工具包等等，现在感觉有点入门了，下面是dollar大神整理的资源，里面有行人检测领域最新最经典的论文，准备利用一两个月把这些经典的论文都读一读，原文地址： http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/files/algorithms.pdf

1. [1]  A. Angelova, A. Krizhevsky, V. Vanhoucke

   Pedestrian Detection with a Large-Field-Of-View Deep Network

   ICRA 2015, Seattle, WA. 1

2. [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. [3]  A. Bar-Hillel, D. Levi, E. Krupka, and C. Goldberg

   Part-based Feature Synthesis for Human Detection

   ECCV 2010, Crete, Greece. 1

4. [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. [5]  R. Benenson, M. Mathias, T. Tuytelaars and L. Van Gool

   Seeking the strongest rigid detector

   CVPR 2013, Portland, OR. 2

6. [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

2

[7] Z. Cai, M. Saberian, and N. Vasconcelos

Learning Complexity-Aware Cascades for Deep Pedestrian Detection

ICCV 2015, Santiago, Chile. 1

[8] G. Chen, Y. Ding, J. Xiao, and T. Han

Detection Evolution with Multi-order Contextual Co-occurrence.

CVPR 2013, Portland, OR. 1

[9] A. D. Costea and S. Nedevschi

Word Channel Based Multiscale Pedestrian DetectionWithout Image Resizing and Using Only One ClassifierCVPR 2014, Columbus, Ohio. 2

[10] A. D. Costea, A. Vesa, and S. Nedevschi

Fast Pedestrian Detection for Mobile Devices

ITSC 2015, Canary Islands. 1

[11] N. Dalal and B. Triggs

Histogram of Oriented Gradient for Human Detection

CVPR 2005, San Diego, California. 1

[12] P. Doll ́ar, R. Appel and W. Kienzle

Crosstalk Cascades for Frame-Rate Pedestrian Detection

ECCV 2012, Florence Italy. 1

[13] P. Doll ́ar, S. Belongie and P. Perona

The Fastest Pedestrian Detector in the West

BMVC 2010, Aberystwyth, UK. 1

[14] P. Doll ́ar, Z. Tu, H. Tao and S. Belongie

Feature Mining for Image Classification

CVPR 2007, Minneapolis, Minnesota. 1

[15] P. Doll ́ar, Z. Tu, P. Perona and S. Belongie

Integral Channel Features

BMVC 2009, London, England. 1

[16] P. Doll ́ar, R. Appel, S. Belongie, and P. Perona

Fast Feature Pyramids for Object Detection

PAMI, 2014. 1

[17] P. Felzenszwalb, D. McAllester, D. Ramanan

A Discriminatively Trained, Multiscale, Deformable Part Model

CVPR 2008, Anchorage, Alaska. 1

[18] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan

Object Detection with Discriminatively Trained Part Based Models

PAMI 2010. 1

[19] J. Hosang, M. Omran, R. Benenson, and B. Schiele

Taking a Deeper Look at Pedestrians

CVPR 2015, Boston, Massachusetts. 2

[20] D. Levi, S. Silberstein, A. Bar-Hillel

Fast multiple-part based object detection using KD-Ferns

CVPR 2013, Portland, OR. 1

3

[21] J. Li, X. Liang, S. Shen, T. Xu, and S. Yan

Scale-aware Fast R-CNN for Pedestrian Detection

arXiv, 2016. 2

[22] 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

[23] Z. Lin and L. Davis

A Pose-Invariant Descriptor for Human Detection and Segmentation

ECCV 2008, Marseille, France. 2

[24] P. Luo, Y. Tian, X. Wang, and X. Tang

Switchable Deep Network for Pedestrian Detection

CVPR 2014, Columbus, Ohio. 2

[25] S. Maji, A. C. Berg, J. Malik

Classification Using Intersection Kernel Support Vector Machines is efficient

CVPR 2008, Anchorage, Alaska. 1

[26] J. Marin, D. Vazquez, A. Lopez, J. Amores, B. Leibe

Random Forests of Local Experts for Pedestrian Detection

ICCV 2013, Sydney, Australia. 2

[27] M. Mathias, R. Benenson, R. Timofte, L. Van Gool

Handling Occlusions with Franken-classifiers

ICCV 2013, Sydney, Australia. 1

[28] W. Nam, B. Han, and J. H. Han

Improving Object Localization Using Macrofeature Layout Selection

ICCV Workshop on Visual Surveillance 2011, Barcelona, Spain. 1

[29] W. Nam, P. Doll ́ar, and J. H. Han

Local Decorrelation For Improved Pedestrian Detection

NIPS 2014, Montreal, Quebec. 1

[30] W. Ouyang and X. Wang

A Discriminative Deep Model for Pedestrian Detection with Occlusion Handling

CVPR 2012, Providence, RI. 1

[31] W. Ouyang and X. Wang

Joint Deep Learning for Pedestrian Detection

ICCV 2013, Sydney, Australia. 1

[32] W. Ouyang and X. Wang

Single-pedestrian detection aided by multi-pedestrian detection.

CVPR 2013, Portland, OR. 1, 2

[33] W. Ouyang, X. Zeng and X. Wang

Modeling Mutual Visibility Relationship with a Deep Model in Pedestrian Detection

CVPR 2013, Portland, OR. 1

[34] 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

4

[35] S. Paisitkriangkrai, C. Shen, A. van den Hengel

Strengthening the Effectiveness of Pedestrian Detection

ECCV 2014, Zurich, Switzerland. 2

[36] S. Paisitkriangkrai, C. Shen, A. van den Hengel

Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning

arXiv, 2014. 2

[37] D. Park, D. Ramanan, C. Fowlkes

Multiresolution models for object detection

ECCV 2010, Crete, Greece. 2

[38] D. Park, C. Lawrence Zitnick, D. Ramanan, P. Doll ́ar

Exploring Weak Stabilization for Motion Feature Extraction

CVPR 2013, Portland, OR. 1

[39] P. Sabzmeydani and G. Mori

Detecting pedestrians by learning shapelet features

CVPR 2007, Minneapolis, Minnesota. 2

[40] W.R. Schwartz, A. Kembhavi, D. Harwood, L. S. Davis

Human Detection Using Partial Least Squares Analysis

ICCV 2009, Kyoto, Japan. 2

[41] P. Sermanet, K. Kavukcuoglu, S. Chintala, Y. LeCun

Pedestrian Detection with Unsupervised Multi-Stage Feature Learning

CVPR 2013, Portland, OR. 1

[42] C. Shen, P. Wang, S. Paisitkriangkrai, A. van den Hengel

Training Effective Node Classifiers for Cascade Classification

IJCV 2013. 1

[43] Y. Tian, P. Luo, X. Wang, and X. Tang

Pedestrian Detection aided by Deep Learning Semantic Tasks

CVPR 2015, Boston, Massachusetts. 2

[44] Y. Tian, P. Luo, X. Wang, and X. Tang

Deep Learning Strong Parts for Pedestrian Detection

ICCV 2015, Santiago, Chile. 1

[45] C. Toca, M. Ciuc, and C. Patrascu

Normalized Autobinomial Markov Channels For Pedestrian Detection

BMVC 2015, Swansea, UK. 2

[46] P. Viola and M. Jones

Robust Real-Time Face Detection

IJCV 2004. 2

[47] S. Walk, N. Majer, K. Schindler, B. Schiele

New Features and Insights for Pedestrian Detection

CVPR 2010, San Francisco, California. 1

[48] X. Wang, T. X. Han, and S. Yan

An HOG-LBP Human Detector with Partial Occlusion Handling

ICCV 2009, Kyoto, Japan. 1

5

[49] C. Wojek and B. Schiele

A Performance Evaluation of Single and Multi-Feature People Detection

DAGM 2008, Munich, Germany. 1, 2

[50] J. Yan, X. Zhang, Z. Lei, S. Liao, S. Z. Li

Robust Multi-Resolution Pedestrian Detection in Traffic Scenes

CVPR 2013, Portland, OR. 1

[51] B. Yang, J. Yan, Z. Lei, and S. Z. Li

Convolutional Channel Features

ICCV 2015, Santiago, Chile. 1

[52] Y. Yang, Z. Wang, and F. Wu

Exploring Prior Knowledge for Pedestrian Detection

BMVC 2015, Swansea, UK. 2

[53] X. Zeng, W. Ouyang, X. Wang

Multi-Stage Contextual Deep Learning for Pedestrian Detection

ICCV 2013, Sydney, Australia. 2

[54] L. Zhang, L. Lin, X. Liang, K. He

Is Faster R-CNN Doing Well for Pedestrian Detection?

ECCV 2016, Amsterdam, The Netherlands. 2

[55] S. Zhang, C. Bauckhage, and A. B. Cremers

Informed Haar-like Features Improve Pedestrian Detection

CVPR 2014, Columbus, Ohio. 1

[56] S. Zhang, R. Benenson, and B. Schiele

Filtered channel features for pedestrian detection

CVPR 2015, Boston, Massachusetts. 1

相信读完以上这些论文后，对行人检测一定会有个比较深入的了解了！