If you have any problems, suggestions or improvements, please submit the issue or PR.

• Misc
• Datasets
• Papers
• Leaderboard

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

• [2019.05] [Chinese Blog] C^3 Framework系列之一：一个基于PyTorch的开源人群计数框架 [Link]
• [2019.04] Crowd counting from scratch [Link]
• [2017.11] Counting Crowds and Lines with AI [Link1] [Link2] [Code]

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

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 Note that all unpublished arXiv papers are not included into the leaderboard of performance.

• NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting [paper][code]
• From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting [paper](extension of S-DCNet)
• AutoScale: Learning to Scale for Crowd Counting [paper](extension of L2SM)
• Domain-adaptive Crowd Counting via Inter-domain Features Segregation and Gaussian-prior Reconstruction [paper]
• Feature-aware Adaptation and Structured Density Alignment for Crowd Counting in Video Surveillance [paper]
• Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network [paper][code]
• Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting [paper]
• Estimating People Flows to Better Count them in Crowded Scenes [paper]
• Segmentation Guided Attention Network for Crowd Counting via Curriculum Learning [paper]
• Deep Density-aware Count Regressor [paper][code]

• [CCWld, SFCN] 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)
• [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]
• [SSR] From Semi-Supervised to Transfer Counting of Crowds (ICCV2013) [paper]

• Beyond Counting：Comparisons of Density Maps for Crowd Analysis Tasks (T-CSVT2018) [paper][arxiv]
• A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (PR Letters2018) [paper]
• Advances and Trends in Visual Crowd Analysis: A Systematic Survey and Evaluation of Crowd Modelling Techniques (Neurocomputing2016) [paper]
• An Evaluation of Crowd Counting Methods, Features and Regression Models (CVIU2015) [paper]
• Crowded Scene Analysis：A Survey (T-CSVT2015) [paper]
• Recent survey on crowd density estimation and counting for visual surveillance (Artificial Intelligence2015) [paper]
• A Survey of Human-Sensing: Methods for Detecting Presence, Count, Location, Track, and Identity (CSUR2010) [paper]

• 3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels (AAAI) [Project]
• [DUBNet] Crowd Counting with Decomposed Uncertainty (AAAI) [paper]
• [CC-Mod] Plug-and-Play Rescaling Based Crowd Counting in Static Images (WACV) [paper]

• [D-ConvNet] Nonlinear Regression via Deep Negative Correlation Learning (T-PAMI) [paper](extension of D-ConvNet)[Project]
• Generalizing semi-supervised generative adversarial networks to regression using feature contrasting (CVIU)[paper]
• [CG-DRCN] Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method (ICCV)[paper]
• [ADMG] Adaptive Density Map Generation for Crowd Counting (ICCV)[paper]
• [DSSINet] Crowd Counting with Deep Structured Scale Integration Network (ICCV) [paper][code]
• [RANet] Relational Attention Network for Crowd Counting (ICCV)[paper]
• [ANF] Attentional Neural Fields for Crowd Counting (ICCV)[paper]
• [SPANet] Learning Spatial Awareness to Improve Crowd Counting (ICCV(oral)) [paper]
• [MBTTBF] Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting (ICCV) [paper]
• [CFF] Counting with Focus for Free (ICCV) [paper][code]
• [L2SM] Learn to Scale: Generating Multipolar Normalized Density Map for Crowd Counting (ICCV) [paper]
• [S-DCNet] From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer (ICCV) [paper][code]
• [BL] Bayesian Loss for Crowd Count Estimation with Point Supervision (ICCV(oral)) [paper][code]
• [PGCNet] Perspective-Guided Convolution Networks for Crowd Counting (ICCV) [paper][code]
• [SACANet] Crowd Counting on Images with Scale Variation and Isolated Clusters (ICCVW) [paper]
• [McML] Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting (ACM MM) [paper]
• [DADNet] DADNet: Dilated-Attention-Deformable ConvNet for Crowd Counting (ACM MM) [paper]
• [MRNet] Crowd Counting via Multi-layer Regression (ACM MM) [paper]
• [MRCNet] MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground Imagery (BMVCW)[paper]
• [E3D] Enhanced 3D convolutional networks for crowd counting (BMVC) [paper]
• [OSSS] One-Shot Scene-Specific Crowd Counting (BMVC) [paper]
• [RAZ-Net] Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization (CVPR) [paper]
• [RDNet] Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization (CVPR) [paper][code]
• [RRSP] Residual Regression with Semantic Prior for Crowd Counting (CVPR) [paper][code]
• [MVMS] Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs (CVPR) [paper] [Project] [Dataset&Code]
• [AT-CFCN] Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting (CVPR) [paper]
• [TEDnet] Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks (CVPR) [paper]
• [CAN] Context-Aware Crowd Counting (CVPR) [paper] [code]
• [PACNN] Revisiting Perspective Information for Efficient Crowd Counting (CVPR)[paper]
• [PSDDN] Point in, Box out: Beyond Counting Persons in Crowds (CVPR(oral))[paper]
• [ADCrowdNet] ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding (CVPR) [paper]
• [CCWld, SFCN] Learning from Synthetic Data for Crowd Counting in the Wild (CVPR) [paper] [Project] [arxiv]
• [DG-GAN] Dense Crowd Counting Convolutional Neural Networks with Minimal Data using Semi-Supervised Dual-Goal Generative Adversarial Networks (CVPRW)[paper]
• [GSP] Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images (CVPRW)[paper]
• [SL2R] Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank (T-PAMI) [paper](extension of L2R)
• [IA-DNN] Inverse Attention Guided Deep Crowd Counting Network (AVSS Best Paper) [paper]
• [MTCNet] MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation (AVSS) [paper]
• [CODA] CODA: Counting Objects via Scale-aware Adversarial Density Adaption (ICME) [paper][code]
• [LSTN] Locality-Constrained Spatial Transformer Network for Video Crowd Counting (ICME(oral)) [paper]
• [DRD] Dynamic Region Division for Adaptive Learning Pedestrian Counting (ICME) [paper]
• [MVSAN] Crowd Counting via Multi-View Scale Aggregation Networks (ICME) [paper]
• [ASD] Adaptive Scenario Discovery for Crowd Counting (ICASSP) [paper]
• [SAAN] Crowd Counting Using Scale-Aware Attention Networks (WACV) [paper]
• [SPN] Scale Pyramid Network for Crowd Counting (WACV) [paper]
• [GWTA-CCNN] Almost Unsupervised Learning for Dense Crowd Counting (AAAI) [paper]
• [GPC] Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation (IROS) [paper]
• [PCC-Net] PCC Net: Perspective Crowd Counting via Spatial Convolutional Network (T-CSVT) [paper] [code]
• [CLPC] Cross-Line Pedestrian Counting Based on Spatially-Consistent Two-Stage Local Crowd Density Estimation and Accumulation (T-CSVT) [paper]
• [MAN] Mask-aware networks for crowd counting (T-CSVT) [paper]
• [CCLL] Crowd Counting With Limited Labeling Through Submodular Frame Selection (T-ITS) [paper]
• [ACSPNet] Atrous convolutions spatial pyramid network for crowd counting and density estimation (Neurocomputing) [paper]
• [DDCN] Removing background interference for crowd counting via de-background detail convolutional network (Neurocomputing) [paper]
• [MRA-CNN] Multi-resolution attention convolutional neural network for crowd counting (Neurocomputing) [paper]
• [ACM-CNN] Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs (Neurocomputing) [paper]
• [SDA-MCNN] Counting crowds using a scale-distribution-aware network and adaptive human-shaped kernel (Neurocomputing) [paper]
• [CAT-CNN] Crowd counting with crowd attention convolutional neural network (Neurocomputing) [paper]
• [DENet] DENet: A Universal Network for Counting Crowd with Varying Densities and Scales (Neurocomputing) [paper][code]
• [SCAR] SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting (Neurocomputing) [paper][code]
• [GMLCNN] Learning Multi-Level Density Maps for Crowd Counting (TNNLS) [paper]
• [HA-CCN] HA-CCN: Hierarchical Attention-based Crowd Counting Network (TIP) [paper]
• [PaDNet] PaDNet: Pan-Density Crowd Counting (TIP) [paper]
• [LDL] Indoor Crowd Counting by Mixture of Gaussians Label Distribution Learning (TIP) [paper]

• [SANet] Scale Aggregation Network for Accurate and Efficient Crowd Counting (ECCV) [paper]
• [ic-CNN] Iterative Crowd Counting (ECCV) [paper]
• [CL] Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (ECCV) [paper]
• [LCFCN] Where are the Blobs: Counting by Localization with Point Supervision (ECCV) [paper] [code]
• [CSR] CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes (CVPR) [paper] [code]
• [L2R] Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (CVPR) [paper] [code]
• [ACSCP] Crowd Counting via Adversarial Cross-Scale Consistency Pursuit (CVPR) [paper] [unofficial code: PyTorch]
• [DecideNet] DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density (CVPR) [paper]
• [AMDCN] An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting (CVPRW) [paper] [code]
• [D-ConvNet] Crowd Counting with Deep Negative Correlation Learning (CVPR) [paper] [code]
• [IG-CNN] Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN (CVPR) [paper]
• [SCNet] In Defense of Single-column Networks for Crowd Counting (BMVC) [paper]
• [AFP] Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid (BMVC) [paper]
• [DRSAN] Crowd Counting using Deep Recurrent Spatial-Aware Network (IJCAI) [paper]
• [TDF-CNN] Top-Down Feedback for Crowd Counting Convolutional Neural Network (AAAI) [paper]
• [CAC] Class-Agnostic Counting (ACCV) [paper] [code]
• [A-CCNN] A-CCNN: Adaptive CCNN for Density Estimation and Crowd Counting (ICIP) [paper]
• Crowd Counting with Fully Convolutional Neural Network (ICIP) [paper]
• [MS-GAN] Multi-scale Generative Adversarial Networks for Crowd Counting (ICPR) [paper]
• [DR-ResNet] A Deeply-Recursive Convolutional Network for Crowd Counting (ICASSP) [paper]
• [GAN-MTR] Crowd Counting With Minimal Data Using Generative Adversarial Networks For Multiple Target Regression (WACV) [paper]
• [SaCNN] Crowd counting via scale-adaptive convolutional neural network (WACV) [paper] [code]
• [Improved SaCNN] Improved Crowd Counting Method Based on Scale-Adaptive Convolutional Neural Network (IEEE Access) [paper]
• [DA-Net] DA-Net: Learning the Fine-Grained Density Distribution With Deformation Aggregation Network (IEEE Access) [paper][code]
• [BSAD] Body Structure Aware Deep Crowd Counting (TIP) [paper]
• [NetVLAD] Multiscale Multitask Deep NetVLAD for Crowd Counting (TII) [paper] [code]
• [W-VLAD] Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps (T-CSVT) [paper]

• [CP-CNN] Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs (ICCV) [paper]
• [ConvLSTM] Spatiotemporal Modeling for Crowd Counting in Videos (ICCV) [paper]
• [CMTL] CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting (AVSS) [paper] [code]
• [ResnetCrowd] ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification (AVSS) [paper]
• [Switching CNN] Switching Convolutional Neural Network for Crowd Counting (CVPR) [paper] [code]
• [DAL-SVR] Boosting deep attribute learning via support vector regression for fast moving crowd counting (PR Letters) [paper]
• [MSCNN] Multi-scale Convolution Neural Networks for Crowd Counting (ICIP) [paper] [code]
• [FCNCC] Fully Convolutional Crowd Counting On Highly Congested Scenes (VISAPP) [paper]

• [Hydra-CNN] Towards perspective-free object counting with deep learning (ECCV) [paper] [code]
• [CNN-Boosting] Learning to Count with CNN Boosting (ECCV) [paper]
• [Crossing-line] Crossing-line Crowd Counting with Two-phase Deep Neural Networks (ECCV) [paper]
• [GP] Gaussian Process Density Counting from Weak Supervision (ECCV) [paper]
• [CrowdNet] CrowdNet: A Deep Convolutional Network for Dense Crowd Counting (ACMMM) [paper] [code]
• [MCNN] Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (CVPR) [paper] [unofficial code: TensorFlow PyTorch]
• [Shang 2016] End-to-end crowd counting via joint learning local and global count (ICIP) [paper]
• [DE-VOC] Fast visual object counting via example-based density estimation (ICIP) [paper]
• [RPF] Crowd Density Estimation based on Rich Features and Random Projection Forest (WACV) [paper]
• [CS-SLR] Cost-sensitive sparse linear regression for crowd counting with imbalanced training data (ICME) [paper]
• [Faster-OHEM-KCF] Deep People Counting with Faster R-CNN and Correlation Tracking (ICME) [paper]

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

• [Arteta 2014] Interactive Object Counting (ECCV) [paper]

• [Idrees 2013] Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images (CVPR) [paper]
• [Ma 2013] Crossing the Line: Crowd Counting by Integer Programming with Local Features (CVPR) [paper]
• [Chen 2013] Cumulative Attribute Space for Age and Crowd Density Estimation (CVPR) [paper]
• [SSR] From Semi-Supervised to Transfer Counting of Crowds (ICCV) [paper]

• [Chen 2012] Feature mining for localised crowd counting (BMVC) [paper]

• [Rodriguez 2011] Density-aware person detection and tracking in crowds (ICCV) [paper]

• [Lempitsky 2010] Learning To Count Objects in Images (NIPS) [paper]

• [Chan 2008] Privacy preserving crowd monitoring: Counting people without people models or tracking (CVPR) [paper]

The section is being continually updated. Note that some values have superscript, which indicates their source.