This is derivative work of xingkongliang's Pedestrian-Detection

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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

>>> Format of Datasets

• 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

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>>> Performance of Caltech

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• KITTI官网

>>> Performance of KITTI

• [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

CVPR19_CSP_Adaptive_NMS

• paper: https://arxiv.org/abs/1904.03629

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

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

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

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• paper: https://arxiv.org/abs/1812.00440
• matlab github: https://github.com/garrickbrazil/AR-Ped

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• arxiv: https://arxiv.org/abs/1709.00235
• paper: https://ieeexplore.ieee.org/document/8328854/

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• arxiv: https://arxiv.org/abs/1510.08160
• paper: https://ieeexplore.ieee.org/abstract/document/8060595/

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• arxiv:
• paper:http://openaccess.thecvf.com/content_ECCV_2018/papers/CHUNLUAN_ZHOU_Bi-box_Regression_for_ECCV_2018_paper.pdf
• slides:
• pytorch-github:https://github.com/rainofmine/Bi-box_Regression

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

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

20180723-OR-CNN

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

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

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• 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:

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

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• arxiv:http://arxiv.org/abs/1711.07752
• project website:
• slides:
• github caffe:
• implemented with SSD pytorch:https://github.com/bailvwangzi/repulsion_loss_ssd

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

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

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

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

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

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

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

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• 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

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

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

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

• paper: https://arxiv.org/abs/1607.07032
• project website:
• matlab implementation on caffe-Faster: https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian
• slides:

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

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

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• 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:

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

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

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