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Awesome Crowd Counting

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

Contents

Misc

Code

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

Technical blog

  • [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]

GT generation

Datasets

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 Note that all unpublished arXiv papers are not included into the leaderboard of performance.

  • Robust Regression via Deep Negative Correlation Learning [paper]
  • Deep Density-aware Count Regressor [paper][code]
  • Video Crowd Counting via Dynamic Temporal Modeling [paper]
  • Dense Scale Network for Crowd Counting [paper][unofficial code: PyTorch]
  • Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection [paper][code]
  • Content-aware Density Map for Crowd Counting and Density Estimation [paper]
  • DENet: A Universal Network for Counting Crowd with Varying Densities and Scales [paper][code]
  • Crowd Transformer Network [paper]
  • W-Net: Reinforced U-Net for Density Map Estimation [paper][code]
  • Crowd Counting with Decomposed Uncertainty [paper]

Methods dealing with the lack of labelled data

  • [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]

Survey

  • 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]
  • Fast crowd density estimation with convolutional neural networks (AI2015) [paper]
  • A Survey of Human-Sensing: Methods for Detecting Presence, Count, Location, Track, and Identity (CSUR2010) [paper]

2019

  • [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]
  • [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) [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]
  • [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]
  • [SCAR] SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting (Neurocomputing) [paper][code]
  • [HA-CCN] HA-CCN: Hierarchical Attention-based Crowd Counting Network (TIP) [paper]
  • [GMLCNN] Learning Multi-Level Density Maps for Crowd Counting (TNNLS) [paper]

2018

  • [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]

2017

  • [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]

2016

  • [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]
  • [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]
  • [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]

2015

  • [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]

2014

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

2013

  • [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]
  • [SSR] From Semi-Supervised to Transfer Counting of Crowds (ICCV) [paper]

2012

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

2010

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

2008

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

Leaderboard

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

ShanghaiTech Part A

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 - - - -
2018--CVPR IG-CNN 72.5 118.2 - - - -
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
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 - - - -
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 SPANet+SANet 59.4 92.5 - - - -
2019--ICCV S-DCNet 58.3 95.0 - - - -
2019--ICCV PGCNet 57.0 86.0 - - - -

ShanghaiTech Part B

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
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--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

UCF-QNRF

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 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--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--ICCV BL 88.7 - 154.8 - - - - - -

UCF_CC_50

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 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

WorldExpo'10

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

UCSD

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

Mall

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

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