人群计数最全代码、数据、论文合集
2021.11.19更新:
人群计数 /Crowd Counting
Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework (Oral)
- 论文/paper:https://arxiv.org/abs/2107.12746
- 代码/code:GitHub - TencentYoutuResearch/CrowdCounting-P2PNet: The official codes for the ICCV2021 Oral presentation "Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework"
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting
- 论文/paper:https://arxiv.org/abs/2107.12619
- 代码/code:https://github.com/TencentYoutuResearch/CrowdCounting-UEPNet
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting
- 论文/paper:https://arxiv.org/abs/2108.08023
- 代码/code:None
以前的:
人群计数最全代码、数据、论文合集(含最新CVPR2019论文)
人群计数最全代码、数据、论文合集(含最新CVPR2019论文)
人群计数最全代码、数据、论文合集
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。
Contents
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 Note that all unpublished arXiv papers are not included into the leaderboard of performance.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
[Chen 2013] Feature mining for localised crowd counting (BMVC2012) [paper]
2010
[Lempitsky 2010] Learning To Count Objects in Images (NIPS2010) [paper]
2008
[Chan 2008] Privacy preserving crowd monitoring: Counting people without people models or tracking (CVPR 2008) [paper]
Leaderboard
阅读原文查看完整Leaderboard
人群计数最全代码、数据、论文合集相关推荐
- 最新最全微光图像增强论文合集
AMiner平台(https://www.aminer.cn)由清华大学计算机系研发,拥有我国完全自主知识产权.平台包含了超过2.3亿学术论文/专利和1.36亿学者的科技图谱,提供学者评价.专家发现. ...
- 识别、提取三维超声中标准平面的总结+论文+代码+数据集+练习合集
目录 数据特点 三维空间定位标准平面 基于监督学习方法 基于强化学习方法 wulalago/LearningNote: some resources on my path in deep learni ...
- 碳中和数据合集:含中国碳中和政策全集、碳中和论文合集
一.碳中和政策 1.数据来源:各省政府官网 2.时间跨度:至今 3.区域范围:全国 4.指标说明: 部分政策下: 名称 部门 发布时间 <十四五"促进中小企业发展规划> 工信部 ...
- 重磅福利!ICCV 2019全部论文合集共1075篇!会议信息全收录!
会议之眼A类,CCF A类的计算机视觉会议ICCV 2019 于11月2日在韩国首尔落下帷幕, 在这场盛会中,华人科学家和企业切切实实地怒刷了一波存在感.会议之眼小助手在这里为大家整理了本次大会信息以 ...
- 还不快收藏起来!何恺明全网最全论文合集
原创/文 BFT机器人 人物简介 何恺明,Facebook AI Research (FAIR) 的一名科学家,研究领域包括计算机视觉和深度学习,并且在计算机视觉和深度学习方面发表了众多极具影响力的论 ...
- 【论文相关】历年CVPR、ICCV、ECCV论文合集下载
历年CVPR.ICCV.ECCV论文合集下载:还在不断更新中 本文来源与更新地址: https://github.com/WingsBrokenAngel/AIPaperCompleteDownloa ...
- 【论文泛读】 Deep Learning 论文合集
[论文泛读] Deep Learning 论文合集 文章目录 [论文泛读] Deep Learning 论文合集 Batch Normalization: Accelerating Deep Netw ...
- 【强化学习论文合集】三十三.2021国际人工智能联合会议论文(IJCAI2021)
欢迎订阅本专栏:<强化学习论文合集> 专栏介绍: 本专栏整理了2017~2022年(后面会持续更新)强化学习领域国际顶级会议已录用的论文,会议包括但不限于:ICML.NeurIPS.AAA ...
- 【强化学习论文合集】十二.2018国际人工智能联合会议论文(IJCAI2018)
欢迎订阅本专栏:<强化学习论文合集> 专栏介绍: 本专栏整理了2017~2022年(后面会持续更新)强化学习领域国际顶级会议已录用的论文,会议包括但不限于:ICML.NeurIPS.AAA ...
最新文章
- 转载 Sqlerver 计算 MD5
- 天池 在线编程 最频繁出现的子串(字符串哈希)
- Hemberg-lab单细胞转录组数据分析(六)
- Hadoop--Yarn常用命令 与 生产环境核心配置参数
- 计算机中桌面指的是什么情况,windows的桌面是指什么
- 理解 RESTful
- 华为新系统鸿蒙,爆料|疑似华为新MatePad Pro包装盒曝光:搭载鸿蒙OS
- 嵌入式系统应用开发—FPGA开发板—一位全加器仿真测试
- 问题:所有播放器打开均提示网络加载失败,有时候浏览器还打不开网页
- 微软 MSCRM 教育成功案例 界面展示
- 工厂模式及在项目中的应用
- c语言函数fac,将一个求阶乘的函数fac专门写在一个文件file1.cpp中,定义为外部函数。然后在另一文件file2.cpp中...
- pygame之窗口大小调整
- lightroom 闪退_微信QQ一碰就闪退,别人永远看不了你的隐私~
- 图与排列、图的存在性
- 保利威HTML5播放器使用文档(参考备用)
- FFmpeg学习笔记之av_parser_parse2()
- 一天吃多少个鸡蛋比较合适?再次强调:不要超过这个量
- 深圳MES系统在智能制造中的应用
- js实现浏览器中的复制粘贴
热门文章
- Shell编程之matrix---装逼又炫酷
- VMware虚拟机安装Centos7
- C 实现strcpy函数
- oracle case grouping,ORACLE GROUPING函數的使用
- mysql 操作审计_利用mysql的audit审计功能记录用户操作信息
- java {@link},Javadoc @see或{@link}?
- 聚合函数的计算机控件,使用Kendo UI MVC Grid包装器的聚合函数
- win10 连接android,win10系统连接安卓手机usb没反应的解决方法
- mysql物理读和逻辑读,SQL Server中STATISTICS IO物理读和逻辑读的误区
- Java中映射怎么实现_我们如何在Java 9的JShell中实现映射?