isance / Pedestrian-Detection

Pedestrian Detection Papers

行人检测（Pedestrian Detection）论文整理

@(论文学习记录)[Paper, Pedestrian Detection]

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相关科研工作者

• Piotr Dollár scholar

• Piotr Dollár homepage

• 张姗姗 scholar

• 张姗姗 homepage

• 欧阳万里 scholar

• 欧阳万里 homepage

Paper List

• [CVPR-2019] High-level Semantic Feature Detection:A New Perspective for Pedestrian Detection
• [CVPR-2019] SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection
• [CVPR-2019] Pedestrian Detection in Thermal Images using Saliency Maps
• [TIP-2018] Too Far to See? Not Really:- Pedestrian Detection with Scale-Aware Localization Policy
• [ECCV-2018] Bi-box Regression for Pedestrian Detection and Occlusion Estimation
• [ECCV-2018] Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting
• [ECCV-2018] Graininess-Aware Deep Feature Learning for Pedestrian Detection
• [ECCV-2018] Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
• [ECCV-2018] Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
• [CVPR-2018] Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors
• [CVPR-2018] Occluded Pedestrian Detection Through Guided Attention in CNNs
• [CVPR-2018] Repulsion Loss: Detecting Pedestrians in a Crowd
• [TCSVT-2018] Pushing the Limits of Deep CNNs for Pedestrian Detection
• [Trans Multimedia-2018] Scale-aware Fast R-CNN for Pedestrian Detection
• [TPAMI-2017] Jointly Learning Deep Features, Deformable Parts, Occlusion and Classification for Pedestrian Detection
• [BMVC-2017] PCN: Part and Context Information for Pedestrian Detection with CNNs
• [CVPR-2017] CityPersons: A Diverse Dataset for Pedestrian Detection
• [CVPR-2017] Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
• [CVPR-2017] What Can Help Pedestrian Detection?
• [ICCV-2017] Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection
• [ICCV-2017] Illuminating Pedestrians via Simultaneous Detection & Segmentation
• [TPAMI-2017] Towards Reaching Human Performance in Pedestrian Detection
• [Transactions on Multimedia-2017] Scale-Aware Fast R-CNN for Pedestrian Detection
• [CVPR-2016] Semantic Channels for Fast Pedestrian Detection
• [CVPR-2016] How Far are We from Solving Pedestrian Detection?
• ![CVPR-2016] Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry
• ![CVPR-2016] Semantic Channels for Fast Pedestrian Detection
• ![ECCV-2016] Is Faster R-CNN Doing Well for Pedestrian Detection?
• [CVPR-2015] Taking a Deeper Look at Pedestrians
• ![ICCV-2015] Learning Complexity-Aware Cascades for Deep Pedestrian Detection
• [ICCV-2015] Deep Learning Strong Parts for Pedestrian Detection
• ![ECCV-2014] Deep Learning of Scene-specific Classifier for Pedestrian Detection
• [CVPR-2013] Joint Deep Learning for Pedestrian Detection
• [CVPR-2012] A Discriminative Deep Model for Pedestrian Detection with Occlusion Handling
• [CVPR-2010] Multi-Cue Pedestrian Classification With Partial Occlusion Handling
• [CVPR-2009] Pedestrian detection: A benchmark
• [CVPR-2008] People-Tracking-by-Detection and People-Detection-by-Tracking
• [ECCV-2006] Human Detection Using Oriented Histograms of Flow and Appearance
• [CVPR-2005] Histograms of Oriented Gradients for Human Detection

行人检测开源代码

论文

[CVPR-2019] Adaptive NMS: Refining Pedestrian Detection in a Crowd

• paper: https://arxiv.org/abs/1904.02948

[CVPR-2019] High-level Semantic Feature Detection:A New Perspective for Pedestrian Detection

• paper: https://arxiv.org/abs/1904.02948
• github: https://github.com/liuwei16/CSP

[CVPR-2019] SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection

• paper: https://arxiv.org/abs/1902.09080v1

[CVPR-2019] Pedestrian Detection in Thermal Images using Saliency Maps

• paper: https://arxiv.org/abs/1904.06859

[TIP-2018] Too Far to See? Not Really:- Pedestrian Detection with Scale-Aware Localization Policy

• arxiv: https://arxiv.org/abs/1709.00235
• paper: https://ieeexplore.ieee.org/document/8328854/
• project website:
• slides:
• github caffe:

[Transactions on Multimedia-2017] Scale-Aware Fast R-CNN for Pedestrian Detection

• arxiv: https://arxiv.org/abs/1510.08160
• paper: https://ieeexplore.ieee.org/abstract/document/8060595/
• project website:
• slides:
• github caffe:

[ECCV-2018] Bi-box Regression for Pedestrian Detection and Occlusion Estimation

• arxiv:
• paper:http://openaccess.thecvf.com/content_ECCV_2018/papers/CHUNLUAN_ZHOU_Bi-box_Regression_for_ECCV_2018_paper.pdf
• slides:
• github:

[ECCV-2018] Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting

• arxiv:
• paper:http://openaccess.thecvf.com/content_ECCV_2018/papers/Wei_Liu_Learning_Efficient_Single-stage_ECCV_2018_paper.pdf
• project website:
• slides:
• github:

[ECCV-2018] Graininess-Aware Deep Feature Learning for Pedestrian Detection

• arxiv:
• paper:http://openaccess.thecvf.com/content_ECCV_2018/papers/Chunze_Lin_Graininess-Aware_Deep_Feature_ECCV_2018_paper.pdf
• project website:
• slides:
• github:

[ECCV-2018] Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

• arxiv: http://arxiv.org/abs/1807.08407

[ECCV-2018] Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation

• arxiv:https://arxiv.org/abs/1807.01438
• project website:
• slides:
• github caffe:

[CVPR-2018] Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors

• arxiv:
• paper: http://vision.snu.ac.kr/projects/partgridnet/data/noh_2018.pdf
• project website: http://vision.snu.ac.kr/projects/partgridnet/
• slides:
• github caffe:

[CVPR-2018] Occluded Pedestrian Detection Through Guided Attention in CNNs

• arxiv:
• paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Occluded_Pedestrian_Detection_CVPR_2018_paper.pdf
• project website:
• slides:
• github caffe:

[CVPR-2018] Repulsion Loss: Detecting Pedestrians in a Crowd

• arxiv:http://arxiv.org/abs/1711.07752
• project website:
• slides:
• github caffe:

[TPAMI-2017] Jointly Learning Deep Features, Deformable Parts, Occlusion and Classification for Pedestrian Detection

• paper: https://ieeexplore.ieee.org/abstract/document/8008790/
• project website:
• slides:
• github caffe:

[BMVC-2017] PCN: Part and Context Information for Pedestrian Detection with CNNs

• arxiv: https://arxiv.org/abs/1804.044838
• project website:
• slides:
• github caffe:

[CVPR-2017] CityPersons: A Diverse Dataset for Pedestrian Detection

• arxiv: http://arxiv.org/abs/1702.05693
• project website:
• slides:
• github caffe:

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[CVPR-2017] Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

• arxiv: https://arxiv.org/abs/1704.02431
• project website:
• slides:
• github caffe:

[CVPR-2017] What Can Help Pedestrian Detection?

• arxiv: https://arxiv.org/abs/1704.02431
• project website:
• slides:
• github caffe:

[TPAMI-2017] Towards Reaching Human Performance in Pedestrian Detection

• paper: http://ieeexplore.ieee.org/document/7917260/
• arxiv:
• project website:
• slides:
• github caffe:

[ICCV-2017] Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection

• paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhou_Multi-Label_Learning_of_ICCV_2017_paper.pdf
• arxiv:
• project website:
• slides:

[ICCV-2017]Illuminating Pedestrians via Simultaneous Detection & Segmentation

• arxiv: https://arxiv.org/abs/1706.08564
• project website: http://cvlab.cse.msu.edu/project-pedestrian-detection.html
• slides:
• github caffe: https://github.com/garrickbrazil/SDS-RCNN

[CVPR-2016] Semantic Channels for Fast Pedestrian Detection

• paper: https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Costea_Semantic_Channels_for_CVPR_2016_paper.pdf
• project website:
• slides:
• github caffe:

[CVPR-2016] Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry

• paper: http://openaccess.thecvf.com/content_cvpr_2016/papers/Cao_Pedestrian_Detection_Inspired_CVPR_2016_paper.pdf
• project website:
• slides:

[CVPR-2016] Semantic Channels for Fast Pedestrian Detection

• paper: http://openaccess.thecvf.com/content_cvpr_2016/papers/Costea_Semantic_Channels_for_CVPR_2016_paper.pdf
• project website:
• slides:

[ECCV-2016] Is Faster R-CNN Doing Well for Pedestrian Detection?

• paper:
• project website:
• slides:

[CVPR-2016] How Far are We from Solving Pedestrian Detection?

• paper: https://www.cv-foundation.org/openaccess/content_cvpr_2016/app/S06-29.pdf
• project website:
• slides:
• github caffe:

[ICCV-2015] Learning Complexity-Aware Cascades for Deep Pedestrian Detection

• paper: http://openaccess.thecvf.com/content_iccv_2015/papers/Cai_Learning_Complexity-Aware_Cascades_ICCV_2015_paper.pdf
• project website:
• slides:

[ICCV-2015] Deep Learning Strong Parts for Pedestrian Detection

• paper: https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Tian_Deep_Learning_Strong_ICCV_2015_paper.htmler.html
• project website:
• slides:
• github caffe:

[CVPR-2013] Joint Deep Learning for Pedestrian Detection Wanli

• paper: https://www.cv-foundation.org/openaccess/content_iccv_2013/html/Ouyang_Joint_Deep_Learning_2013_ICCV_paper.html
• project website:
• slides:
• github caffe:

[CVPR-2012] A Discriminative Deep Model for Pedestrian Detection with Occlusion Handling

• paper: http://mmlab.ie.cuhk.edu.hk/pdf/ouyangWcvpr2012.pdf
• paper: https://ieeexplore.ieee.org/abstract/document/6248062/
• project website:
• slides:
• github caffe:

[CVPR-2010] Multi-Cue Pedestrian Classification With Partial Occlusion Handling

• paper: https://ieeexplore.ieee.org/abstract/document/5540111/
• project website:
• slides:
• github caffe:

行人检测数据集

CityPersons

CityPersons数据集是在Cityscapes数据集基础上建立的，使用了Cityscapes数据集的数据，对一些类别进行了精确的标注。该数据集是在[CVPR-2017] CityPersons: A Diverse Dataset for Pedestrian Detection这篇论文中提出的，更多细节可以通过阅读该论文了解。

上图中左侧是行人标注，右侧是原始的CityScapes数据集。

• 标注和评估文件
• 数据集下载 文件格式

#评测文件
$/Cityscapes/shanshanzhang-citypersons/evaluation/eval_script/coco.py
$/Cityscapes/shanshanzhang-citypersons/evaluation/eval_script/eval_demo.py
$/Cityscapes/shanshanzhang-citypersons/evaluation/eval_script/eval_MR_multisetup.py

#注释文件
$/Cityscapes/shanshanzhang-citypersons/annotations
$/Cityscapes/shanshanzhang-citypersons/annotations/anno_train.mat
$/Cityscapes/shanshanzhang-citypersons/annotations/anno_val.mat
$/Cityscapes/shanshanzhang-citypersons/annotations/README.txt
#图片数据

$/Cityscapes/leftImg8bit/train/*
$/Cityscapes/leftImg8bit/val/*
$/Cityscapes/leftImg8bit/test/*

注释文件格式

CityPersons annotations
(1) data structure:
    one image per cell
    in each cell, there are three fields: city_name; im_name; bbs (bounding box annotations)

(2) bounding box annotation format:
   one object instance per row:
   [class_label, x1,y1,w,h, instance_id, x1_vis, y1_vis, w_vis, h_vis]

(3) class label definition:
  class_label =0: ignore regions (fake humans, e.g. people on posters, reflections etc.)
    class_label =1: pedestrians
    class_label =2: riders
    class_label =3: sitting persons
    class_label =4: other persons with unusual postures
    class_label =5: group of people

(4) boxes:
  visible boxes [x1_vis, y1_vis, w_vis, h_vis] are automatically generated from segmentation masks;
      (x1,y1) is the upper left corner.
      if class_label==1 or 2
        [x1,y1,w,h] is a well-aligned bounding box to the full body ;
      else
        [x1,y1,w,h] = [x1_vis, y1_vis, w_vis, h_vis];

Caltech

• Caltech官网 更所细节请阅读这篇论文， [TAPAMI-2012] Pedestrian Detection: An Evaluation of the State of the Art

KITTI

• KITTI官网 

性能比较

Method	MR (Reasonable)	MR (Reasonable_small)	MR (Reasonable_occ=heavy)	MR (All)
OR-CNN	11.32%	14.19%	51.43%	40.19%
Repultion Loss	11.48%	15.67%	52.59%	39.17%
Adapted FasterRCNN	12.97%	37.24%	50.47%	43.86%