Official PyTorch implementation of the ICIP 2022 paper FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion.

The code was writen in Python 3.9.10. The PyTorch version used is 1.10.0

ShanghaiTech A & B can be found on Kaggle.

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

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}
}