/Awesome-Crowd-Counting

Awesome Crowd Counting

Awesome Crowd Counting

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Contents

Tools

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 Note that all unpublished arXiv papers are not included into the leaderboard of performance.

  • PaDNet: Pan-Density Crowd Counting [paper]
  • Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance [paper][code]
  • In Defense of Single-column Networks for Crowd Counting [paper]
  • Perspective-Aware CNN For Crowd Counting [paper]
  • Attention to Head Locations for Crowd Counting [paper]
  • Crowd Counting with Density Adaption Networks [paper]
  • Geometric and Physical Constraints for Head Plane Crowd Density Estimation in Videos [paper]
  • Improving Object Counting with Heatmap Regulation [paper][code]
  • Depth Information Guided Crowd Counting for Complex Crowd Scenes [paper]
  • Structured Inhomogeneous Density Map Learning for Crowd Counting [paper]

2018

  • [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]
  • [DecideNet] DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density (CVPR2018) [paper]
  • [DR-ResNet] A Deeply-Recursive Convolutional Network for Crowd Counting (ICASSP2018) [paper]
  • [SaCNN] Crowd counting via scale-adaptive convolutional neural network (WACV2018) [paper] [code]
  • [GAN-MTR] Crowd Counting With Minimal Data Using Generative Adversarial Networks For Multiple Target Regression (WACV2018) [paper]
  • [NetVLAD] Multiscale Multitask Deep NetVLAD for Crowd Counting (TII2018) [paper] [code]

2017

  • [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]
  • A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (PR Letters) [paper]
  • [MSCNN] Multi-scale Convolution Neural Networks for Crowd Counting (ICIP2017) [paper] [code]
  • [FCNCC] Fully Convolutional Crowd Counting On Highly Congested Scenes (VISAPP2017) [paper]

2016

  • [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]

2015

  • [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]
  • [Fu 2015] Fast crowd density estimation with convolutional neural networks (AI2015) [paper]

2013

  • [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]

2012

  • [Chen 2013] Feature mining for localised crowd counting (BMVC2012) [paper]

2010

  • [Lempitsky 2010] Learning To Count Objects in Images (NIPS2010) [paper]

2008

  • [Chan 2008] Privacy preserving crowd monitoring: Counting people without people models or tracking (CVPR 2008) [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--ICIP MSCNN 83.8 127.4 - - - -
2017--AVSS CMTL 101.3 152.4 - - - None
2017--CVPR Switching CNN 90.4 135.0 - - - VGG-16
2017--ICCV CP-CNN 73.6 106.4 - - - -
2018--WACV SaCNN 86.8 139.2 - - - -
2018--CVPR ACSCP 75.7 102.7 - - 5.1M None
2018--CVPR CSRNet 68.2 115.0 23.79 0.76 16.26MSANet VGG-16
2018--CVPR IG-CNN 72.5 118.2 - - - -
2018--CVPR D-ConvNet-v1 73.5 112.3 - - - -
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--ECCV SANet 67.0 104.5 - - 0.91M None
2018--AAAI TDF-CNN 97.5 145.1 - - - -

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--ECCV ic-CNN (one stage) 10.4 16.7
2018--ECCV ic-CNN (two stages) 10.7 16
2018--ECCV SANet 8.4 13.6
2018--AAAI TDF-CNN 20.7 32.8

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

UCF_CC_50

Year-Conference/Journal Methods MAE MSE
2013--CVPR Idrees 2013 468 590.3
2015--CVPR Zhang 2015 467.0 498.5
2016--CVPR MCNN 377.6 509.1
2016--ACM MM CrowdNet 452.5 -
2016--ECCV Hydra-CNN 333.73 425.26
2016--ECCV CNN-Boosting 364.4 -
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 ConvLSTM-nt 284.5 297.1
2017--ICCV CP-CNN 298.8 320.9
2018--TIP BSAD 409.5 563.7
2018--WACV SaCNN 314.9 424.8
2018--CVPR ACSCP 291.0 404.6
2018--CVPR CSRNet 266.1 397.5
2018--CVPR IG-CNN 291.4 349.4
2018--CVPR D-ConvNet-v1 288.4 404.7
2018--CVPR L2R (Multi-task, Query-by-example) 291.5 397.6
2018--CVPR L2R (Multi-task, Keyword) 279.6 388.9
2018--IJCAI DRSAN 219.2 250.2
2018--ECCV ic-CNN (two stages) 260.9 365.5
2018--ECCV SANet 258.4 334.9
2018--AAAI TDF-CNN 354.7 491.4

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 8.1 11.6
2017--ICIP MSCNN 7.8 15.4 14.9 11.8 5.8 11.7
2017--CVPR Switching CNN 4.4 15.7 10 11 5.9 9.4
2017--ICCV ConvLSTM-nt 8.6 16.9 14.6 15.4 4 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--ICCV CP-CNN 2.9 14.7 10.5 10.4 5.8 8.86
2018--TIP BSAD 4.1 21.7 11.9 11 3.5 10.5
2018--WACV SaCNN 2.6 13.5 10.6 12.5 3.3 8.5
2018--CVPR ACSCP 2.8 14.05 9.6 8.1 2.9 7.5
2018--CVPR CSRNet 2.9 11.5 8.6 16.6 3.4 8.6
2018--CVPR IG-CNN 2.6 16.1 10.15 20.2 7.6 11.3
2018--CVPR D-ConvNet-v1 1.9 12.1 20.7 8.3 2.6 9.1
2018--CVPR DecideNet 2 13.14 8.9 17.4 4.75 9.23
2018--IJCAI DRSAN 2.6 11.8 10.3 10.4 3.7 7.76
2018--ECCV ic-CNN (two stages) 17 12.3 9.2 8.1 4.7 10.3
2018--ECCV SANet 2.6 13.2 9 13.3 3 8.2
2018--AAAI TDF-CNN 2.7 23.4 10.7 17.6 3.3 11.5

UCSD

Year-Conference/Journal Method MAE MSE
2015--CVPR Zhang 2015 1.60 3.31
2016--CVPR MCNN 1.07 1.35
2016--ECCV Hydra-CNN 1.65 -
2016--ECCV CNN-Boosting 1.10 -
2017--CVPR Switching CNN 1.62 2.10
2017--ICCV ConvLSTM-nt 1.73 3.52
2017--ICCV ConvLSTM 1.30 1.79
2017--ICCV Bidirectional ConvLSTM 1.13 1.43
2018--TIP BSAD 1.0 1.40
2018--CVPR ACSCP 1.04 1.35
2018--CVPR CSRNet 1.16 1.47
2018--ECCV SANet 1.02 1.29