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- [2020.04] The JHU-CROWD++ Dataset is released.
- [C^3 Framework] An open-source PyTorch code for crowd counting, which is released.
- [Chinese Blog] 人群计数论文解读 [Link]
- [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]
Please refer to this page.
Considering the increasing number of papers in this field, we roughly summarize some articles and put them into the following categories (they are still listed in this document):
Note that all unpublished arXiv papers are not included in the leaderboard of performance.
- Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting [paper]
- Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions [paper][code]
- Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting [paper][code]
- JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method [paper]
- Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting [paper]
- Understanding the impact of mistakes on background regions in crowd counting [paper]
- CNN-based Density Estimation and Crowd Counting: A Survey [paper]
- Efficient Crowd Counting via Structured Knowledge Transfer [paper]
- Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting [paper][code]
- Drone Based RGBT Vehicle Detection and Counting: A Challenge [paper]
- NAS-Count: Counting-by-Density with Neural Architecture Search [paper]
- Towards Using Count-level Weak Supervision for Crowd Counting [paper]
- NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization [paper][code]
Earlier ArXiv Papers
- PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting [paper]
- 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]
- Fast Video Crowd Counting with a Temporal Aware Network [paper]
- Dense Scale Network for Crowd Counting [paper][unofficial code: PyTorch]
- Content-aware Density Map for Crowd Counting and Density Estimation [paper]
- Crowd Transformer Network [paper]
- W-Net: Reinforced U-Net for Density Map Estimation [paper][code]
- 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]
- Attention to Head Locations for Crowd Counting [paper]
- Crowd Counting with Density Adaption Networks [paper]
- Improving Object Counting with Heatmap Regulation [paper][code]
- Structured Inhomogeneous Density Map Learning for Crowd Counting [paper]
- Image Crowd Counting Using Convolutional Neural Network and Markov Random Field [paper] [code]
- [LSC-CNN] Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection (T-PAMI) [paper][code]
- [ASNet] Attention Scaling for Crowd Counting (CVPR) [paper] [code]
- [HSRNet] Crowd Counting via Hierarchical Scale Recalibration Network (ECAI) [paper]
- [CWAN] Weakly Supervised Crowd-Wise Attention For Robust Crowd Counting (ICASSP) [paper]
- [AGRD] Attention Guided Region Division for Crowd Counting (ICASSP) [paper]
- [BBA-NET] BBA-NET: A Bi-Branch Attention Network For Crowd Counting (ICASSP) [paper]
- [SMANet] Stochastic Multi-Scale Aggregation Network for Crowd Counting (ICASSP) [paper]
- [MSPNET] Stacked Pooling For Boosting Scale Invariance Of Crowd Counting (ICASSP) [paper] [arxiv] [code]
- [MSPNET] Multi-supervised Parallel Network for Crowd Counting (ICASSP) [paper]
- [ASPDNet] Counting dense objects in remote sensing images (ICASSP) [paper]
- [FSC] Focus on Semantic Consistency for Cross-domain Crowd Understanding (ICASSP) [paper]
- [C-CNN] A Real-Time Deep Network for Crowd Counting (ICASSP) [paper]
- [HyGnn] Hybrid Graph Neural Networks for Crowd Counting (AAAI) [paper]
- [DUBNet] Crowd Counting with Decomposed Uncertainty (AAAI) [paper]
- [SDANet] Shallow Feature based Dense Attention Network for Crowd Counting (AAAI) [paper]
- [3DCC] 3D Crowd Counting via Multi-View Fusion with 3D Gaussian Kernels (AAAI) [paper][Project]
- [FSSA] Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning (WACV) [paper]
- [CC-Mod] Plug-and-Play Rescaling Based Crowd Counting in Static Images (WACV) [paper]
- [CLPNet] Cross-Level Parallel Network for Crowd Counting (TII) [paper]
- [HA-CCN] HA-CCN: Hierarchical Attention-based Crowd Counting Network (TIP) [paper]
- [PaDNet] PaDNet: Pan-Density Crowd Counting (TIP) [paper]
- [MS-GAN] Adversarial Learning for Multiscale Crowd Counting Under Complex Scenes (TCYB) [paper]
- [ZoomCount] ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images (T-CSVT) [paper]
- [DensityCNN] Density-Aware Multi-Task Learning for Crowd Counting (TMM) [paper]
- [FMLF] Crowd Density Estimation Using Fusion of Multi-Layer Features (TITS) [paper]
- [MLSTN] Multi-level feature fusion based Locality-Constrained Spatial Transformer network for video crowd counting (Neurocomputing) [paper](extension of LSTN)
- [SRN+PS] Scale-Recursive Network with point supervision for crowd scene analysis (Neurocomputing) [paper]
- [ASDF] Counting crowds with varying densities via adaptive scenario discovery framework (Neurocomputing) [paper](extension of ASD)
- [CAT-CNN] Crowd counting with crowd attention convolutional neural network (Neurocomputing) [paper]
- [RRP] Relevant Region Prediction for Crowd Counting (Neurocomputing) [paper]
- [SCAN] Crowd Counting via Scale-Communicative Aggregation Networks (Neurocomputing) [paper](extension of MVSAN)
- [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]
- [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]
- [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]
- [ACNN] Incorporating Side Information by Adaptive Convolution (NIPS) [paper][Project]
- [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.
| Year-Conference/Journal | Methods | MAE | MSE | PSNR | SSIM | Params | Pre-trained Model |
|---|---|---|---|---|---|---|---|
| 2016--CVPR | MCNN | 110.2 | 173.2 | 21.4CSR | 0.52CSR | 0.13MSANet | None |
| 2017--AVSS | CMTL | 101.3 | 152.4 | - | - | - | None |
| 2017--CVPR | Switching CNN | 90.4 | 135.0 | - | - | 15.11MSANet | VGG-16 |
| 2017--ICIP | MSCNN | 83.8 | 127.4 | - | - | - | - |
| 2017--ICCV | CP-CNN | 73.6 | 106.4 | 21.72CP-CNN | 0.72CP-CNN | 68.4MSANet | - |
| 2018--AAAI | TDF-CNN | 97.5 | 145.1 | - | - | - | - |
| 2018--WACV | SaCNN | 86.8 | 139.2 | - | - | - | - |
| 2018--CVPR | ACSCP | 75.7 | 102.7 | - | - | 5.1M | None |
| 2018--CVPR | D-ConvNet-v1 | 73.5 | 112.3 | - | - | - | VGG-16 |
| 2018--CVPR | IG-CNN | 72.5 | 118.2 | - | - | - | VGG-16 |
| 2018--CVPR | L2R (Multi-task, Query-by-example) | 72.0 | 106.6 | - | - | - | VGG-16 |
| 2018--CVPR | L2R (Multi-task, Keyword) | 73.6 | 112.0 | - | - | - | VGG-16 |
| 2019--CVPRW | GSP (one stage, efficient) | 70.7 | 103.6 | - | - | - | VGG-16 |
| 2018--IJCAI | DRSAN | 69.3 | 96.4 | - | - | - | - |
| 2018--ECCV | ic-CNN (one stage) | 69.8 | 117.3 | - | - | - | - |
| 2018--ECCV | ic-CNN (two stages) | 68.5 | 116.2 | - | - | - | - |
| 2018--CVPR | CSRNet | 68.2 | 115.0 | 23.79 | 0.76 | 16.26MSANet | VGG-16 |
| 2018--ECCV | SANet | 67.0 | 104.5 | - | - | 0.91M | None |
| 2019--AAAI | GWTA-CCNN | 154.7 | 229.4 | - | - | - | - |
| 2019--ICASSP | ASD | 65.6 | 98.0 | - | - | - | - |
| 2019--ICCV | CFF | 65.2 | 109.4 | 25.4 | 0.78 | - | - |
| 2019--CVPR | SFCN | 64.8 | 107.5 | - | - | - | - |
| 2019--ICCV | SPN+L2SM | 64.2 | 98.4 | - | - | - | - |
| 2019--CVPR | TEDnet | 64.2 | 109.1 | 25.88 | 0.83 | 1.63M | - |
| 2019--CVPR | ADCrowdNet(AMG-bAttn-DME) | 63.2 | 98.9 | 24.48 | 0.88 | - | - |
| 2019--CVPR | PACNN | 66.3 | 106.4 | - | - | - | - |
| 2019--CVPR | PACNN+CSRNet | 62.4 | 102.0 | - | - | - | - |
| 2019--CVPR | CAN | 62.3 | 100.0 | - | - | - | VGG-16 |
| 2019--TIP | HA-CCN | 62.9 | 94.9 | - | - | - | - |
| 2019--ICCV | BL | 62.8 | 101.8 | - | - | - | - |
| 2019--WACV | SPN | 61.7 | 99.5 | - | - | - | - |
| 2019--ICCV | DSSINet | 60.63 | 96.04 | - | - | - | - |
| 2019--ICCV | MBTTBF-SCFB | 60.2 | 94.1 | - | - | - | - |
| 2019--ICCV | RANet | 59.4 | 102.0 | - | - | - | - |
| 2019--ICCV | SPANet+SANet | 59.4 | 92.5 | - | - | - | - |
| 2019--TIP | PaDNet | 59.2 | 98.1 | - | - | - | - |
| 2019--ICCV | S-DCNet | 58.3 | 95.0 | - | - | - | - |
| 2019--ICCV | PGCNet | 57.0 | 86.0 | - | - | - | - |
| Year-Conference/Journal | Methods | MAE | MSE |
|---|---|---|---|
| 2016--CVPR | MCNN | 26.4 | 41.3 |
| 2017--ICIP | MSCNN | 17.7 | 30.2 |
| 2017--AVSS | CMTL | 20.0 | 31.1 |
| 2017--CVPR | Switching CNN | 21.6 | 33.4 |
| 2017--ICCV | CP-CNN | 20.1 | 30.1 |
| 2018--TIP | BSAD | 20.2 | 35.6 |
| 2018--WACV | SaCNN | 16.2 | 25.8 |
| 2018--CVPR | ACSCP | 17.2 | 27.4 |
| 2018--CVPR | CSRNet | 10.6 | 16.0 |
| 2018--CVPR | IG-CNN | 13.6 | 21.1 |
| 2018--CVPR | D-ConvNet-v1 | 18.7 | 26.0 |
| 2018--CVPR | DecideNet | 21.53 | 31.98 |
| 2018--CVPR | DecideNet + R3 | 20.75 | 29.42 |
| 2018--CVPR | L2R (Multi-task, Query-by-example) | 14.4 | 23.8 |
| 2018--CVPR | L2R (Multi-task, Keyword) | 13.7 | 21.4 |
| 2018--IJCAI | DRSAN | 11.1 | 18.2 |
| 2018--AAAI | TDF-CNN | 20.7 | 32.8 |
| 2018--ECCV | ic-CNN (one stage) | 10.4 | 16.7 |
| 2018--ECCV | ic-CNN (two stages) | 10.7 | 16.0 |
| 2019--CVPRW | GSP (one stage, efficient) | 9.1 | 15.9 |
| 2018--ECCV | SANet | 8.4 | 13.6 |
| 2019--WACV | SPN | 9.4 | 14.4 |
| 2019--ICCV | PGCNet | 8.8 | 13.7 |
| 2019--ICASSP | ASD | 8.5 | 13.7 |
| 2019--CVPR | TEDnet | 8.2 | 12.8 |
| 2019--TIP | HA-CCN | 8.1 | 13.4 |
| 2019--TIP | PaDNet | 8.1 | 12.2 |
| 2019--ICCV | RANet | 7.9 | 12.9 |
| 2019--CVPR | CAN | 7.8 | 12.2 |
| 2019--CVPR | ADCrowdNet(AMG-attn-DME) | 7.7 | 12.9 |
| 2019--CVPR | ADCrowdNet(AMG-DME) | 7.6 | 13.9 |
| 2019--CVPR | SFCN | 7.6 | 13.0 |
| 2019--CVPR | PACNN | 8.9 | 13.5 |
| 2019--CVPR | PACNN+CSRNet | 7.6 | 11.8 |
| 2019--ICCV | BL | 7.7 | 12.7 |
| 2019--ICCV | CFF | 7.2 | 12.2 |
| 2019--ICCV | SPN+L2SM | 7.2 | 11.1 |
| 2019--ICCV | DSSINet | 6.85 | 10.34 |
| 2019--ICCV | S-DCNet | 6.7 | 10.7 |
| 2019--ICCV | SPANet+SANet | 6.5 | 9.9 |
| Year-Conference/Journal | Method | C-MAE | C-NAE | C-MSE | DM-MAE | DM-MSE | DM-HI | L- Av. Precision | L-Av. Recall | L-AUC |
|---|---|---|---|---|---|---|---|---|---|---|
| 2013--CVPR | Idrees 2013CL | 315 | 0.63 | 508 | - | - | - | - | - | - |
| 2016--CVPR | MCNNCL | 277 | 0.55 | 426 | 0.006670 | 0.0223 | 0.5354 | 59.93% | 63.50% | 0.591 |
| 2017--AVSS | CMTLCL | 252 | 0.54 | 514 | 0.005932 | 0.0244 | 0.5024 | - | - | - |
| 2017--CVPR | Switching CNNCL | 228 | 0.44 | 445 | 0.005673 | 0.0263 | 0.5301 | - | - | - |
| 2018--ECCV | CL | 132 | 0.26 | 191 | 0.00044 | 0.0017 | 0.9131 | 75.8% | 59.75% | 0.714 |
| 2019--TIP | HA-CCN | 118.1 | - | 180.4 | - | - | - | - | - | - |
| 2019--CVPR | TEDnet | 113 | - | 188 | - | - | - | - | - | - |
| 2019--ICCV | RANet | 111 | - | 190 | - | - | - | - | - | - |
| 2019--CVPR | CAN | 107 | - | 183 | - | - | - | - | - | - |
| 2019--ICCV | SPN+L2SM | 104.7 | - | 173.6 | - | - | - | - | - | - |
| 2019--ICCV | S-DCNet | 104.4 | - | 176.1 | - | - | - | - | - | - |
| 2019--CVPR | SFCN | 102.0 | - | 171.4 | - | - | - | - | - | - |
| 2019--ICCV | DSSINet | 99.1 | - | 159.2 | - | - | - | - | - | - |
| 2019--ICCV | MBTTBF-SCFB | 97.5 | - | 165.2 | - | - | - | - | - | - |
| 2019--TIP | PaDNet | 96.5 | - | 170.2 | - | - | - | - | - | - |
| 2019--ICCV | BL | 88.7 | - | 154.8 | - | - | - | - | - | - |
| Year-Conference/Journal | Methods | MAE | MSE |
|---|---|---|---|
| 2013--CVPR | Idrees 2013 | 468.0 | 590.3 |
| 2015--CVPR | Zhang 2015 | 467.0 | 498.5 |
| 2016--ACM MM | CrowdNet | 452.5 | - |
| 2016--CVPR | MCNN | 377.6 | 509.1 |
| 2016--ECCV | CNN-Boosting | 364.4 | - |
| 2016--ECCV | Hydra-CNN | 333.73 | 425.26 |
| 2016--ICIP | Shang 2016 | 270.3 | - |
| 2017--ICIP | MSCNN | 363.7 | 468.4 |
| 2017--AVSS | CMTL | 322.8 | 397.9 |
| 2017--CVPR | Switching CNN | 318.1 | 439.2 |
| 2017--ICCV | CP-CNN | 298.8 | 320.9 |
| 2017--ICCV | ConvLSTM-nt | 284.5 | 297.1 |
| 2018--TIP | BSAD | 409.5 | 563.7 |
| 2018--AAAI | TDF-CNN | 354.7 | 491.4 |
| 2018--WACV | SaCNN | 314.9 | 424.8 |
| 2018--CVPR | IG-CNN | 291.4 | 349.4 |
| 2018--CVPR | ACSCP | 291.0 | 404.6 |
| 2018--CVPR | L2R (Multi-task, Query-by-example) | 291.5 | 397.6 |
| 2018--CVPR | L2R (Multi-task, Keyword) | 279.6 | 388.9 |
| 2018--CVPR | D-ConvNet-v1 | 288.4 | 404.7 |
| 2018--CVPR | CSRNet | 266.1 | 397.5 |
| 2018--ECCV | ic-CNN (two stages) | 260.9 | 365.5 |
| 2018--ECCV | SANet | 258.4 | 334.9 |
| 2018--IJCAI | DRSAN | 219.2 | 250.2 |
| 2019--AAAI | GWTA-CCNN | 433.7 | 583.3 |
| 2019--WACV | SPN | 259.2 | 335.9 |
| 2019--CVPR | ADCrowdNet(DME) | 257.1 | 363.5 |
| 2019--TIP | HA-CCN | 256.2 | 348.4 |
| 2019--CVPR | TEDnet | 249.4 | 354.5 |
| 2019--CVPR | PACNN | 267.9 | 357.8 |
| 2019--CVPR | PACNN+CSRNet | 241.7 | 320.7 |
| 2019--ICCV | RANet | 239.8 | 319.4 |
| 2019--ICCV | MBTTBF-SCFB | 233.1 | 300.9 |
| 2019--ICCV | BL | 229.3 | 308.2 |
| 2019--ICCV | DSSINet | 216.9 | 302.4 |
| 2019--CVPR | SFCN | 214.2 | 318.2 |
| 2019--CVPR | CAN | 212.2 | 243.7 |
| 2019--ICCV | S-DCNet | 204.2 | 301.3 |
| 2019--ICASSP | ASD | 196.2 | 270.9 |
| 2019--ICCV | SPN+L2SM | 188.4 | 315.3 |
| 2019--TIP | PaDNet | 185.8 | 278.3 |
| Year-Conference/Journal | Method | S1 | S2 | S3 | S4 | S5 | Avg. |
|---|---|---|---|---|---|---|---|
| 2015--CVPR | Zhang 2015 | 9.8 | 14.1 | 14.3 | 22.2 | 3.7 | 12.9 |
| 2016--CVPR | MCNN | 3.4 | 20.6 | 12.9 | 13.0 | 8.1 | 11.6 |
| 2017--ICIP | MSCNN | 7.8 | 15.4 | 14.9 | 11.8 | 5.8 | 11.7 |
| 2017--ICCV | ConvLSTM-nt | 8.6 | 16.9 | 14.6 | 15.4 | 4.0 | 11.9 |
| 2017--ICCV | ConvLSTM | 7.1 | 15.2 | 15.2 | 13.9 | 3.5 | 10.9 |
| 2017--ICCV | Bidirectional ConvLSTM | 6.8 | 14.5 | 14.9 | 13.5 | 3.1 | 10.6 |
| 2017--CVPR | Switching CNN | 4.4 | 15.7 | 10.0 | 11.0 | 5.9 | 9.4 |
| 2017--ICCV | CP-CNN | 2.9 | 14.7 | 10.5 | 10.4 | 5.8 | 8.86 |
| 2018--AAAI | TDF-CNN | 2.7 | 23.4 | 10.7 | 17.6 | 3.3 | 11.5 |
| 2018--CVPR | IG-CNN | 2.6 | 16.1 | 10.15 | 20.2 | 7.6 | 11.3 |
| 2018--TIP | BSAD | 4.1 | 21.7 | 11.9 | 11.0 | 3.5 | 10.5 |
| 2018--ECCV | ic-CNN | 17.0 | 12.3 | 9.2 | 8.1 | 4.7 | 10.3 |
| 2018--CVPR | DecideNet | 2.0 | 13.14 | 8.9 | 17.4 | 4.75 | 9.23 |
| 2018--CVPR | D-ConvNet-v1 | 1.9 | 12.1 | 20.7 | 8.3 | 2.6 | 9.1 |
| 2018--CVPR | CSRNet | 2.9 | 11.5 | 8.6 | 16.6 | 3.4 | 8.6 |
| 2018--WACV | SaCNN | 2.6 | 13.5 | 10.6 | 12.5 | 3.3 | 8.5 |
| 2018--ECCV | SANet | 2.6 | 13.2 | 9.0 | 13.3 | 3.0 | 8.2 |
| 2018--IJCAI | DRSAN | 2.6 | 11.8 | 10.3 | 10.4 | 3.7 | 7.76 |
| 2018--CVPR | ACSCP | 2.8 | 14.05 | 9.6 | 8.1 | 2.9 | 7.5 |
| 2019--ICCV | PGCNet | 2.5 | 12.7 | 8.4 | 13.7 | 3.2 | 8.1 |
| 2019--CVPR | TEDnet | 2.3 | 10.1 | 11.3 | 13.8 | 2.6 | 8.0 |
| 2019--CVPR | PACNN | 2.3 | 12.5 | 9.1 | 11.2 | 3.8 | 7.8 |
| 2019--CVPR | ADCrowdNet(AMG-bAttn-DME) | 1.7 | 14.4 | 11.5 | 7.9 | 3.0 | 7.7 |
| 2019--CVPR | ADCrowdNet(AMG-attn-DME) | 1.6 | 13.2 | 8.7 | 10.6 | 2.6 | 7.3 |
| 2019--CVPR | CAN | 2.9 | 12.0 | 10.0 | 7.9 | 4.3 | 7.4 |
| 2019--CVPR | CAN(ECAN) | 2.4 | 9.4 | 8.8 | 11.2 | 4.0 | 7.2 |
| 2019--ICCV | DSSINet | 1.57 | 9.51 | 9.46 | 10.35 | 2.49 | 6.67 |
| Year-Conference/Journal | Method | MAE | MSE |
|---|---|---|---|
| 2015--CVPR | Zhang 2015 | 1.60 | 3.31 |
| 2016--ECCV | Hydra-CNN | 1.65 | - |
| 2016--ECCV | CNN-Boosting | 1.10 | - |
| 2016--CVPR | MCNN | 1.07 | 1.35 |
| 2017--ICCV | ConvLSTM-nt | 1.73 | 3.52 |
| 2017--CVPR | Switching CNN | 1.62 | 2.10 |
| 2017--ICCV | ConvLSTM | 1.30 | 1.79 |
| 2017--ICCV | Bidirectional ConvLSTM | 1.13 | 1.43 |
| 2018--CVPR | CSRNet | 1.16 | 1.47 |
| 2018--CVPR | ACSCP | 1.04 | 1.35 |
| 2018--ECCV | SANet | 1.02 | 1.29 |
| 2018--TIP | BSAD | 1.00 | 1.40 |
| 2019--WACV | SPN | 1.03 | 1.32 |
| 2019--ICCV | SPANet+SANet | 1.00 | 1.28 |
| 2019--CVPR | ADCrowdNet(DME) | 0.98 | 1.25 |
| 2019--BMVC | E3D | 0.93 | 1.17 |
| 2019--CVPR | PACNN | 0.89 | 1.18 |
| 2019--TIP | PaDNet | 0.85 | 1.06 |
| Year-Conference/Journal | Method | MAE | MSE |
|---|---|---|---|
| 2012--BMVC | Chen 2012 | 3.15 | 15.7 |
| 2016--ECCV | CNN-Boosting | 2.01 | - |
| 2017--ICCV | ConvLSTM-nt | 2.53 | 11.2 |
| 2017--ICCV | ConvLSTM | 2.24 | 8.5 |
| 2017--ICCV | Bidirectional ConvLSTM | 2.10 | 7.6 |
| 2018--CVPR | DecideNet | 1.52 | 1.90 |
| 2018--IJCAI | DRSAN | 1.72 | 2.1 |
| 2019--BMVC | E3D | 1.64 | 2.13 |
| 2019--WACV | SAAN | 1.28 | 1.68 |