/Awesome-Crowd-Counting

Awesome Crowd Counting

Awesome Crowd Counting

Papers

2018

  • Crowd Counting using Deep Recurrent Spatial-Aware Network (IJCAI2018) [paper]
  • Top-Down Feedback for Crowd Counting Convolutional Neural Network (AAAI2018) [paper]
  • Scale Aggregation Network for Accurate and Efficient Crowd Counting (ECCV2018) [paper]
  • Iterative Crowd Counting (ECCV2018) [paper]
  • Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (ECCV2018) [paper]
  • Crowd Counting with Deep Negative Correlation Learning (CVPR2018) [paper] [code]
  • Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN (CVPR2018) [paper]
  • Structured Inhomogeneous Density Map Learning for Crowd Counting (arXiv) [paper]
  • Body Structure Aware Deep Crowd Counting (TIP2018) [paper]
  • CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes (CVPR2018) [paper] [code]
  • Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (CVPR2018) [paper] [code]
  • Crowd Counting via Adversarial Cross-Scale Consistency Pursuit (CVPR2018) [paper]
  • DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density (CVPR2018) [paper]
  • Crowd counting via scale-adaptive convolutional neural network (WACV2018) [paper] [code]

2017

  • Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs (ICCV2017) [paper]
  • Spatiotemporal Modeling for Crowd Counting in Videos (ICCV2017) [paper]
  • CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting (AVSS2017) [paper] [code]
  • 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]
  • Image Crowd Counting Using Convolutional Neural Network and Markov Random Field (arXiv) [paper] [code]
  • Multi-scale Convolution Neural Networks for Crowd Counting (arXiv) [paper] [code]

2016

  • Towards perspective-free object counting with deep learning (ECCV2016) [paper] [code]
  • Slicing Convolutional Neural Network for Crowd Video Understanding (CVPR2016) [paper] [code]
  • CrowdNet: A Deep Convolutional Network for Dense Crowd Counting (CVPR2016) [paper] [code]
  • Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (CVPR2016) [paper] [code] [unofficial code]

2015

  • COUNT Forest: CO-voting Uncertain Number of Targets using Random Forest for Crowd Density Estimation (ICCV2015) [paper]
  • Cross-scene Crowd Counting via Deep Convolutional Neural Networks (CVPR2015) [paper] [code]

2013

  • Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images (CVPR2013) [paper]
  • Crossing the Line: Crowd Counting by Integer Programming with Local Features (CVPR2013) [paper]

2012

  • Feature mining for localised crowd counting (ECCV2012) [paper]

2008

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

Datasets

Performance

The section is being continually updated.

ShanghaiTech Part A

Method MAE MSE PSNR SSIM Model Size Params Runtime (ms) Pre-trained
DAN 81.8 134.7 - - - - - -
CSR 68.2 115.0 23.79 0.76 - - - -
L2R 73.6 112.0 - - - - - -
ACSCP 75.7 102.7 - - 5.1M - - -
MCNN 110.2 173.2 21.4 0.52 0.12M - - -

ShanghaiTech Part B

Method MAE MSE
DAN 13.2 20.1
BSAD 20.2 35.6
CSR 10.6 16.0
MCNN 26.4 41.3
L2R 13.7 21.4
DecideNet 21.53 31.98
ACSCP 17.2 27.4

UCF_CC_50

Method MAE MSE
DAN 309.6 402.64
BSAD 409.5 563.7
CSR 266.1 397.5
L2R 279.6 388.9
ACSCP 291.0 404.6

WorldExpo'10

Method S1 S2 S3 S4 S5 Avg.
DAN 4.1 11.1 10.7 16.2 5.0 9.4
BSAD 4.1 21.7 11.9 11.0 3.5 10.5
CSR 2.9 11.5 8.6 16.6 3.4 8.6
DecideNet 2.0 13.14 8.90 17.40 4.75 9.23
ACSCP 2.8 14.05 9.6 8.1 2.9 7.5

UCSD

Method MAE MSE
BSAD 1.00 1.40
CSR 1.16 1.47
ACSCP 1.04 1.35

Tools