  Crowd counting is a fundamental problem in crowd analysis which is typically
accomplished by estimating a crowd density map and summing over the density
values. However, this approach suffers from background noise accumulation and
loss of density due to the use of broad Gaussian kernels to create the ground
truth density maps. This issue can be overcome by narrowing the Gaussian
kernel. However, existing approaches perform poorly when trained with ground
truth density maps with broad kernels. To deal with this limitation, we propose
using conditional diffusion models to predict density maps, as diffusion models
show high fidelity to training data during generation. With that, we present
$CrowdDiff$ that generates the crowd density map as a reverse diffusion
process. Furthermore, as the intermediate time steps of the diffusion process
are noisy, we incorporate a regression branch for direct crowd estimation only
during training to improve the feature learning. In addition, owing to the
stochastic nature of the diffusion model, we introduce producing multiple
density maps to improve the counting performance contrary to the existing crowd
counting pipelines. We conduct extensive experiments on publicly available
datasets to validate the effectiveness of our method. $CrowdDiff$ outperforms
existing state-of-the-art crowd counting methods on several public crowd
analysis benchmarks with significant improvements.
