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[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 ]
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 Note that all unpublished arXiv papers are not included into the leaderboard of performance .
Video Crowd Counting via Dynamic Temporal Modeling [paper ]
Dense Scale Network for Crowd Counting [paper ]
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 ]
Counting with Focus for Free [paper ]
Crowd Transformer Network [paper ]
W-Net: Reinforced U-Net for Density Map Estimation [paper ][code ]
Crowd Counting with Decomposed Uncertainty [paper ]
Generalizing semi-supervised generative adversarial networks to regression using feature contrasting [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 ]
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 ]
[RAZ-Net] Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization (CVPR2019 ) [paper ]
[RDNet] Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization (CVPR2019 ) [paper ]
[RRSP] Residual Regression with Semantic Prior for Crowd Counting (CVPR2019 ) [paper ]
[MVMS] Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs (CVPR2019 ) [paper ]
[AT-CFCN] Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting (CVPR2019 ) [paper ]
[TEDnet] Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks (CVPR2019 ) [paper ]
[CAN] Context-Aware Crowd Counting (CVPR2019 ) [paper ] [unofficial code: PyTorch ]
[PACNN] Revisiting Perspective Information for Efficient Crowd Counting (CVPR2019 )[paper ]
[PSDDN] Point in, Box out: Beyond Counting Persons in Crowds (CVPR2019 )[paper ]
[ADCrowdNet] ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding (CVPR2019 ) [paper ]
[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 )
[IA-DNN] Inverse Attention Guided Deep Crowd Counting Network (AVSS2019 ) [paper ]
[CODA] CODA: Counting Objects via Scale-aware Adversarial Density Adaption (ICME2019 ) [paper ][code ]
[LSTN] Locality-Constrained Spatial Transformer Network for Video Crowd Counting (ICME2019 ) [paper ]
Dynamic Region Division for Adaptive Learning Pedestrian Counting (ICME2019 )
Crowd Counting via Multi-View Scale Aggregation Networks (ICME2019 )
[ASD] Adaptive Scenario Discovery for Crowd Counting (ICASSP2019 ) [paper ]
[SAAN] Crowd Counting Using Scale-Aware Attention Networks (WACV2019 ) [paper ]
[SPN] Scale Pyramid Network for Crowd Counting (WACV2019 ) [paper ]
[GWTA-CCNN] Almost Unsupervised Learning for Dense Crowd Counting (AAAI2019 ) [paper ]
[PCC-Net] PCC Net: Perspective Crowd Counting via Spatial Convolutional Network (T-CSVT2019 ) [paper ] [code ]
Cross-Line Pedestrian Counting Based on Spatially-Consistent Two-Stage Local Crowd Density Estimation and Accumulation (T-CSVT2019 ) [paper ]
[LCFCN] Where are the Blobs: Counting by Localization with Point Supervision (ECCV2018 ) [paper ] [code ]
[CAC] Class-Agnostic Counting (ACCV2018 ) [paper ] [code ]
[SCNet] In Defense of Single-column Networks for Crowd Counting (BMVC2018 ) [paper ]
[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 ] [unofficial code: PyTorch ]
[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 ]
Crowd Counting with Fully Convolutional Neural Network (ICIP2018 ) [paper ]
[MS-GAN] Multi-scale Generative Adversarial Networks for Crowd Counting (ICPR2018 ) [paper ]
[DR-ResNet] A Deeply-Recursive Convolutional Network for Crowd Counting (ICASSP2018 ) [paper ]
[GAN-MTR] Crowd Counting With Minimal Data Using Generative Adversarial Networks For Multiple Target Regression (WACV2018 ) [paper ]
[SaCNN] Crowd counting via scale-adaptive convolutional neural network (WACV2018 ) [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 ]
[NetVLAD] Multiscale Multitask Deep NetVLAD for Crowd Counting (TII2018 ) [paper ] [code ]
[W-VLAD] Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps (T-CSVT2018 ) [paper ]
[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 ]
[DAL-SVR] Boosting deep attribute learning via support vector regression for fast moving crowd counting (PR Letters2017 ) [paper ]
[MSCNN] Multi-scale Convolution Neural Networks for Crowd Counting (ICIP2017 ) [paper ] [code ]
[FCNCC] Fully Convolutional Crowd Counting On Highly Congested Scenes (VISAPP2017 ) [paper ]
[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 ]
[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 ]
[Arteta 2014] Interactive Object Counting (ECCV2014 ) [paper ]
[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 ]
[SSR] From Semi-Supervised to Transfer Counting of Crowds (ICCV2013 ) [paper ]
[Chen 2012] Feature mining for localised crowd counting (BMVC2012 ) [paper ]
[Lempitsky 2010] Learning To Count Objects in Images (NIPS2010 ) [paper ]
[Chan 2008] Privacy preserving crowd monitoring: Counting people without people models or tracking (CVPR 2008 ) [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
-
-
-
-
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--CVPR
SFCN
64.8
107.5
-
-
-
-
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--WACV
SPN
61.7
99.5
-
-
-
-
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--ICASSP
ASD
8.5
13.7
2019--CVPR
TEDnet
8.2
12.8
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
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 2013 CL
315
0.63
508
-
-
-
-
-
-
2016--CVPR
MCNN CL
277
0.55
0.006670
0.0223
0.5354
59.93%
63.50%
0.591
2017--AVSS
CMTL CL
252
0.54
514
0.005932
0.0244
0.5024
-
-
-
2017--CVPR
Switching CNN CL
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--CVPR
TEDnet
113
-
188
-
-
-
-
-
-
2019--CVPR
CAN
107
-
183
-
-
-
-
-
-
2019--CVPR
SFCN
102.0
-
171.4
-
-
-
-
-
-
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--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