Awesome
FusionCount
Official PyTorch implementation of the ICIP 2022 paper FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion.
Requirements
The code was writen in Python 3.9.10. The PyTorch version used is 1.10.0
Datasets
ShanghaiTech A & B can be found on Kaggle.
Model Structure
Training
Please refer to DM-Count for data preparation and training details. Please notice that you will also have to normalise the predicted density map, as illustrated in the code snippet from DM-Count/models.py below.
...
mu = self.density_layer(x)
B, C, H, W = mu.size()
mu_sum = mu.view([B, -1]).sum(1).unsqueeze(1).unsqueeze(2).unsqueeze(3)
mu_normed = mu / (mu_sum + 1e-6)
return mu, mu_normed
Results
| Model | Multi-Adds | MAE (SHA) | RMSE (SHA) | MAE (SHB) | RMSE (SHB) |
|---|---|---|---|---|---|
| CSRNet | 856.99 G | 68.2 | 115.0 | 10.6 | 16.0 |
| CAN | 908.05 G | 62.3 | 100.0 | 7.8 | 12.2 |
| BL | 853.70 G | 62.8 | 101.8 | 7.7 | 12.7 |
| DM-Count | 853.70 G | 59.7 | 95.7 | 7.4 | 11.8 |
| FusionCount (ours) | 815.00 G | 62.2 | 101.2 | 6.9 | 11.8 |
Citation
The paper has been accepted by ICIP 2022 and published on IEEE Xplore. You can also find the arXiv version here. Please cite us if you find it useful!
@INPROCEEDINGS{9897322,
author={Ma, Yiming and Sanchez, Victor and Guha, Tanaya},
booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
title={Fusioncount: Efficient Crowd Counting Via Multiscale Feature Fusion},
year={2022},
volume={},
number={},
pages={3256-3260},
doi={10.1109/ICIP46576.2022.9897322}
}