If you have any problems, suggestions or improvements, please submit the issue or PR.
- Density Map Generation from Key Points [Matlab Code] [Python Code] [Fast Python Code]
- UCF-QNRF Dataset [Link]
- ShanghaiTech Dataset [Link: Dropbox / BaiduNetdisk]
- WorldExpo'10 Dataset [Link]
- UCF CC 50 Dataset [Link]
- Mall Dataset [Link]
- UCSD Dataset [Link]
- SmartCity Dataset [Link: GoogleDrive / BaiduNetdisk]
- AHU-Crowd Dataset [Link]
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 Note that all unpublished arXiv papers are not included into the leaderboard of performance.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]
- [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]
- [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]
- [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]
-
[Hydra-CNN] Towards perspective-free object counting with deep learning (ECCV2016) [paper] [code]
-
[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]
- [COUNT Forest] COUNT Forest: CO-voting Uncertain Number of Targets using Random Forest for Crowd Density Estimation (ICCV2015) [paper]
- [Zhang 2015] Cross-scene Crowd Counting via Deep Convolutional Neural Networks (CVPR2015) [paper] [code]
- [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]
- [Chen 2013] Feature mining for localised crowd counting (BMVC2012) [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--ICIP | MSCNN | 83.8 | 127.4 | - | - | - | - |
| 2017--AVSS | CMTL | 101.3 | 152.4 | - | - | - | None |
| 2017--CVPR | Switching CNN | 90.4 | 135 | - | - | - | 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 | 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 | 106.6 | - | - | - | VGG-16 |
| 2018--CVPR | L2R (Multi-task, Keyword) | 73.6 | 112 | - | - | - | 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 | - | - | - | - |
| Year-Conference/Journal | Methods | MAE | MSE |
|---|---|---|---|
| 2016--CVPR | MCNN | 26.4 | 41.3 |
| 2017--ICIP | MSCNN | 17.7 | 30.2 |
| 2017--AVSS | CMTL | 20 | 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 |
| 2018--CVPR | IG-CNN | 13.6 | 21.1 |
| 2018--CVPR | D-ConvNet-v1 | 18.7 | 26 |
| 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 |
| 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 |
| Year-Conference/Journal | Methods | MAE | MSE |
|---|---|---|---|
| 2013--CVPR | Idrees 2013 | 468 | 590.3 |
| 2015--CVPR | Zhang 2015 | 467 | 498.5 |
| 2016--CVPR | MCNN | 377.6 | 509.1 |
| 2016--ACM MM | CrowdNet | 452.5 | - |
| 2016--ECCV | Hydra-CNN | 333.7 | 425.2 |
| 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 | 295.8 | 320.9 |
| 2018-TIP | BSAD | 409.5 | 563.7 |
| 2018-WACV | SaCNN | 314.9 | 424.8 |
| 2018--CVPR | ACSCP | 291 | 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 |
| 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 |
| Year-Conference/Journal | Method | MAE | MSE |
|---|---|---|---|
| 2015--CVPR | Zhang 2015 | 1.6 | 3.31 |
| 2016--CVPR | MCNN | 1.07 | 1.35 |
| 2017--CVPR | Switching CNN | 1.62 | 2.1 |
| 2017--ICCV | ConvLSTM-nt | 1.73 | 3.52 |
| 2017--ICCV | ConvLSTM | 1.3 | 1.79 |
| 2017--ICCV | Bidirectional ConvLSTM | 1.13 | 1.43 |
| 2018-TIP | BSAD | 1.0 | 1.4 |
| 2018--CVPR | ACSCP | 1.04 | 1.35 |
| 2018--CVPR | CSRNet | 1.07 | 1.35 |
| 2018--ECCV | SANet | 1.02 | 1.29 |