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}
}