Awesome Crowd Counting

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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

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

Datasets

Free-view

Name	Year	Attributes	Avg. Resolution	No. Samples	No. Instenaces	Avg. Cnt	Link
NWPU-Crowd	2020	Congested	2311*3383	5,109	2,133,238	418	[Homepage] [Download] [Code]
JHU-CROWD	2019	Congested	1450*900	4,250	1,114,785	262	Unreleased
UCF-QNRF	2018	Congested	2013*2902	1,535	1,251,642	815	[Homepage] [Download]
ShanghaiTech Part A	2016	Congested	589*868	482	241,677	501	Download: [Link1] [Link2]
UCF_CC_50	2013	Congested	2101*2888	50	63,974	1,279	[Homepage]

Surveillance-view

Name	Year	Attributes	Avg. Resolution	No. Samples	No. Instenaces	Avg. Cnt	Link
Crowd Surveillance	2019	Free scenes	840*1342	13,945	386,513	28	[Homepage]
ShanghaiTechRGBD	2019	Depth	-	-	-	-	[Homepage]
Fudan-ShanghaiTech	2019	Video	1080*1920	15,000	394,081	27	[Homepage] [Download (pwd:sgt1)]
GCC	2019	400 Fixed Scenes, Synthetic	1080*1920	15,211	7,625,843	501	Download: [Link1] [Link2] [Link3 (pwd:utdo)]
Venice	2019	4 Fixed Scenes	720*1280	167	-	-	[Download]
CityStreet	2019	Multi-view	1520*2704	500	-	-	[Homepage]
Beijing-BRT	2019	1 Fixed Scene	640*360	1,280	16,795	13	[Homepage]
SmartCity	2018	-	1080*1920	50	369	7	Download: [Link1] [Link2]
CityUHK-X	2017	55 Fixed Scenes	384*512	3,191	106,783	33	[Homepage]
ShanghaiTech Part B	2016	Free Scenes	768*1024	716	88,488	123	Download: [Link1] [Link2]
AHU-Crowd	2016	-	720*576	107	45,000	421	[Homepage]
WorldExpo'10	2015	108 Fixed Scenes	576*720	3,980	199,923	50	[Homepage]
Mall	2012	1 Fixed Scene	480*640	2,000	62,325	31	[Homepage]
UCSD	2008	1 Fixed Scene	158*238	2,000	49,885	25	[Homepage]

Drone-view

Name	Year	Attributes	Avg. Resolution	No. Samples	No. Instenaces	Avg. Cnt	Link
DroneVehicle	2020	Vehicle	840*712	31,064	441,642	14.2	[Homepage]
DroneCrowd	2019	Video	1080*1920	33,600	4,864,280	145	[Homepage]
DLR-ACD	2019	-	-	33	226,291	6,857	[Homepage]

Papers

arXiv papers

Note that all unpublished arXiv papers are not included in the leaderboard of performance.

Efficient Crowd Counting via Structured Knowledge Transfer [paper]
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting [paper]
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]
ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images [paper]
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting [paper][code]
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]

Earlier ArXiv Papers

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]
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]
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]
Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance [paper][code]
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]

Methods dealing with the lack of labelled data

[FSC] Focus on Semantic Consistency for Cross-domain Crowd Understanding (ICASSP) [paper]
[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]
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]

2020

[HSRNet] Crowd Counting via Hierarchical Scale Recalibration Network (ECAI) [paper]
[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]
[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]

2019

[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]

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

[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]

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]
[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]

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]
[FU 2015] Fast crowd density estimation with convolutional neural networks (Artificial Intelligence) [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]
[Chen 2013] Cumulative Attribute Space for Age and Crowd Density Estimation (CVPR) [paper]
[SSR] From Semi-Supervised to Transfer Counting of Crowds (ICCV) [paper]

2012

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

2011

[Rodriguez 2011] Density-aware person detection and tracking in crowds (ICCV) [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	21.4^CSR	0.52^CSR	0.13M^SANet	None
2017--AVSS	CMTL	101.3	152.4	-	-	-	None
2017--CVPR	Switching CNN	90.4	135.0	-	-	15.11M^SANet	VGG-16
2017--ICIP	MSCNN	83.8	127.4	-	-	-	-
2017--ICCV	CP-CNN	73.6	106.4	21.72^CP-CNN	0.72^CP-CNN	68.4M^SANet	-
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	16.26M^SANet	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	-	-	-	-

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
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

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 2013^CL	315	0.63	508	-	-	-	-	-	-
2016--CVPR	MCNN^CL	277	0.55	426	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--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	-	-	-	-	-	-

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	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

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
2019--TIP	PaDNet	0.85	1.06

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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