【资源】人群计数最全资源(含代码、工具、数据集、论文、leaderboard)

前言

之前极市曾分享了几个GitHub上的awesome系列项目,反响都很好。


今日分享一个人群计数超全资源。近年来,由于拥挤人群引发的踩踏事故频发,人群计数在视频监控、公共安全方面的作用越发突出,以下是作者整理的人群计数资源,包含代码、工具、数据集、论文、leaderboard等。
作者:gjy3035
来源:https://github.com/gjy3035/Awesome-Crowd-Counting

Contents


Code

Crowd Counting Code Framework (C^3 Framework)

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

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 Note that all unpublished arXiv papers are not included into the leaderboard of performance.

  • Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks [paper]
  • Generalizing semi-supervised generative adversarial networks to regression using feature contrasting [paper]
  • 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]
  • Mask-aware networks for crowd counting [paper]
  • ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding [paper]
  • Context-Aware Crowd Counting [paper]
  • PaDNet: Pan-Density Crowd Counting [paper]


    Methods dealing with the lack of labelled data

  • [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)
  • [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]


2019

  • [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)
  • [ASD] Adaptive Scenario Discovery for Crowd Counting (ICASSP2019) [paper]
  • Crowd Counting Using Scale-Aware Attention Networks (WACV2019) [paper]
  • [GWTA-CCNN] Almost Unsupervised Learning for Dense Crowd Counting (AAAI2019) [paper]


2018

  • [LCFCN] Where are the Blobs: Counting by Localization with Point Supervision (ECCV2018) [paper] [code]
  • [CAC] Class-Agnostic Counting (ACCV2018) [paper code]
  • [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]
  • [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]
  • [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]
  • [W-VLAD] Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps (CSVT2018) [paper]


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 - - - -
2019--AAAI GWTA-CCNN 154.7 229.4 - - - -
2019--ICASSP ASD 65.6 98.0 - - - -


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--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
2018--AAAI TDF-CNN 20.7 32.8
2019--ICASSP ASD 8.5 13.7


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 468.0 590.3
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
2019--AAAI GWTA-CCNN 433.7 583.3
2019--ICASSP ASD 196.2 270.9


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--CVPR Switching CNN 4.4 15.7 10.0 11.0 5.9 9.4
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--ICCV CP-CNN 2.9 14.7 10.5 10.4 5.8 8.86
2018--TIP BSAD 4.1 21.7 11.9 11.0 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 2.0 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) 17.0 12.3 9.2 8.1 4.7 10.3
2018--ECCV SANet 2.6 13.2 9.0 13.3 3.0 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





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