人群计数最全代码、数据、论文合集（含最新CVPR2019论文）

2019 年 3 月 14 日 极市平台

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前言

之前极市曾分享了几个GitHub上的awesome系列项目，反响都很好（点击文末阅读原文即可获取以下资源）。

【资源】手势估计最全资源
【资源】多目标追踪资源列表
【资源】OCR 文本检测干货汇总
【资源】语义分割 paper 以及 code 汇总
【资源】视频研究常用方法、数据集和任务汇总

今日分享一个人群计数超全资源。近年来，由于拥挤人群引发的踩踏事故频发，人群计数在视频监控、公共安全方面的作用越发突出，以下是作者整理的人群计数资源，包含代码、工具、数据集、论文、leaderboard等。

作者：gjy3035

来源：https://github.com/gjy3035/Awesome-Crowd-Counting

注：本文涉及较多超链接，请点击文末阅读原文，以获得更好的阅读体验。

Code
Tools
Datasets
Papers
Leaderboard

Code

Crowd Counting Code Framework (C^3 Framework)

[C^3 Framework] An open-source PyTorch code for crowd counting, which is under development.

Tools

Density Map Generation from Key Points [Matlab Code] [Python Code] [Fast Python Code]

Datasets

GCC Dataset [Link] (a large-scale, synthetic and diverse dataset)
UCF-QNRF Dataset [Link]
ShanghaiTech Dataset [Link: Dropbox / BaiduNetdisk]
WorldExpo'10 Dataset [Link]
UCF CC 50 Dataset [Link]
Mall Dataset [Link]
UCSD Dataset [Link]
SmartCity Dataset [Link: GoogleDrive / BaiduNetdisk]
AHU-Crowd Dataset [Link]

Papers

arXiv papers

This section only includes the last ten papers since 2018 in arXiv.org. Previous papers will be hidden using . If you want to view them, please open the raw file to read the source code. Note that all unpublished arXiv papers are not included into the leaderboard of performance.

Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks [paper]
Generalizing semi-supervised generative adversarial networks to regression using feature contrasting [paper]
Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling [paper]
Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting [paper]
Scale-Aware Attention Network for Crowd Counting [paper]
Mask-aware networks for crowd counting [paper]
ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding [paper]
Context-Aware Crowd Counting [paper]
PaDNet: Pan-Density Crowd Counting [paper]

Methods dealing with the lack of labelled data

[CCWld] Learning from Synthetic Data for Crowd Counting in the Wild (CVPR2019) [paper] [Project] [arxiv]) 本文解读请关注极市今日推送二条
[SL2R] Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank (T-PAMI) [paper](extension of L2R)
[GWTA-CCNN] Almost Unsupervised Learning for Dense Crowd Counting (AAAI2019) [paper]
[CAC] Class-Agnostic Counting (ACCV2018) [paper code]
[L2R] Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (CVPR2018) [paper] [code]

2019

[CCWld] Learning from Synthetic Data for Crowd Counting in the Wild (CVPR2019) [paper] [Project] [arxiv])
[SL2R] Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank (T-PAMI) [paper](extension of L2R)
[ASD] Adaptive Scenario Discovery for Crowd Counting (ICASSP2019) [paper]
Crowd Counting Using Scale-Aware Attention Networks (WACV2019) [paper]
[GWTA-CCNN] Almost Unsupervised Learning for Dense Crowd Counting (AAAI2019) [paper]

2018

[LCFCN] Where are the Blobs: Counting by Localization with Point Supervision (ECCV2018) [paper] [code]
[CAC] Class-Agnostic Counting (ACCV2018) [paper code]
[AFP] Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid (BMVC2018) [paper]
[DRSAN] Crowd Counting using Deep Recurrent Spatial-Aware Network (IJCAI2018) [paper]
[TDF-CNN] Top-Down Feedback for Crowd Counting Convolutional Neural Network (AAAI2018) [paper]
[SANet] Scale Aggregation Network for Accurate and Efficient Crowd Counting (ECCV2018) [paper]
[ic-CNN] Iterative Crowd Counting (ECCV2018) [paper]
[CL] Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (ECCV2018) [paper]
[D-ConvNet] Crowd Counting with Deep Negative Correlation Learning (CVPR2018) [paper] [code]
[IG-CNN] Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN (CVPR2018) [paper]
[BSAD] Body Structure Aware Deep Crowd Counting (TIP2018) [paper]
[CSR] CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes (CVPR2018) [paper] [code]
[L2R] Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (CVPR2018) [paper] [code]
[ACSCP] Crowd Counting via Adversarial Cross-Scale Consistency Pursuit (CVPR2018) [paper]
[DecideNet] DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density (CVPR2018) [paper]
[AMDCN] An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting (CVPR2018) [paper] [code]
[A-CCNN] A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting (ICIP2018) [paper]
[DR-ResNet] A Deeply-Recursive Convolutional Network for Crowd Counting (ICASSP2018) [paper]
[SaCNN] Crowd counting via scale-adaptive convolutional neural network (WACV2018) [paper] [code]
[GAN-MTR] Crowd Counting With Minimal Data Using Generative Adversarial Networks For Multiple Target Regression (WACV2018) [paper]
[NetVLAD] Multiscale Multitask Deep NetVLAD for Crowd Counting (TII2018) [paper] [code]
[W-VLAD] Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps (CSVT2018) [paper]

2017

[CP-CNN] Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs (ICCV2017) [paper]
[ConvLSTM] Spatiotemporal Modeling for Crowd Counting in Videos (ICCV2017) [paper]
[CMTL] CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting (AVSS2017) [paper] [code]
[ResnetCrowd] ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification (AVSS2017) [paper]
[Switching CNN] Switching Convolutional Neural Network for Crowd Counting (CVPR2017) [paper] [code]
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (PR Letters) [paper]
[MSCNN] Multi-scale Convolution Neural Networks for Crowd Counting (ICIP2017) [paper] [code]
[FCNCC] Fully Convolutional Crowd Counting On Highly Congested Scenes (VISAPP2017) [paper]

2016

[Hydra-CNN] Towards perspective-free object counting with deep learning (ECCV2016) [paper] [code]
[CNN-Boosting] Learning to Count with CNN Boosting (ECCV2016) [paper]
[Crossing-line] Crossing-line Crowd Counting with Two-phase Deep Neural Networks (ECCV2016) [paper]
[CrowdNet] CrowdNet: A Deep Convolutional Network for Dense Crowd Counting (ACMMM2016) [paper] [code]
[MCNN] Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (CVPR2016) [paper] [unofficial code: TensorFlow PyTorch]
[Shang 2016] End-to-end crowd counting via joint learning local and global count (ICIP2016) [paper]
[RPF] Crowd Density Estimation based on Rich Features and Random Projection Forest (WACV2016) [paper]
[CS-SLR] Cost-sensitive sparse linear regression for crowd counting with imbalanced training data (ICME2016) [paper]
[Faster-OHEM-KCF] Deep People Counting with Faster R-CNN and Correlation Tracking (ICME2016) [paper]

2015

[COUNT Forest] COUNT Forest: CO-voting Uncertain Number of Targets using Random Forest for Crowd Density Estimation (ICCV2015) [paper]
[Bayesian] Bayesian Model Adaptation for Crowd Counts (ICCV2015) [paper]
[Zhang 2015] Cross-scene Crowd Counting via Deep Convolutional Neural Networks (CVPR2015) [paper] [code]
[Wang 2015] Deep People Counting in Extremely Dense Crowds (ACMMM2015) [paper]
[Fu 2015] Fast crowd density estimation with convolutional neural networks (AI2015) [paper]

2013

[Idrees 2013] Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images (CVPR2013) [paper]
[Ma 2013] Crossing the Line: Crowd Counting by Integer Programming with Local Features (CVPR2013) [paper]

2012