【科研】行人检测 | Pedestrian Detection历年论文及项目总结

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行人检测

一、相关科研工作者

• Piotr Dollár

• 张姗姗

• 欧阳万里

二、历年优秀论文

• [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

三、行人检测开源代码

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

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

• code: https://github.com/liuwei16/CSP

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

• paper: http://vision.snu.ac.kr/projects/partgridnet/data/noh_2018.pdf

• project: http://vision.snu.ac.kr/projects/partgridnet/

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

• paper: https://arxiv.org/abs/1706.08564

• project : http://cvlab.cse.msu.edu/project-pedestrian-detection.html

• code: https://github.com/garrickbrazil/SDS-RCNN

[CVPR-2018] Repulsion Loss: DetectingPedestrians in a Crowd

• paper:http://arxiv.org/abs/1711.07752

• code:https://github.com/rainofmine/Repulsion_Loss

四、行人检测数据集

1. CityPersons

https://www.cityscapes-dataset.com

2. Caltech

http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/

3. KITTI

http://www.cvlibs.net/datasets/kitti/

4. EuroCity

https://eurocity-dataset.tudelft.nl/eval/overview/statistics

5. CrowdHuman

http://www.crowdhuman.org

五、主流算法性能

最后，祝大家炼丹愉快，科研顺利~~