  Crowd counting has recently attracted significant attention in the field of
computer vision due to its wide applications to image understanding. Numerous
methods have been proposed and achieved state-of-the-art performance for
real-world tasks. However, existing approaches do not perform well under
adverse weather such as haze, rain, and snow since the visual appearances of
crowds in such scenes are drastically different from those images in clear
weather of typical datasets. In this paper, we propose a method for robust
crowd counting in adverse weather scenarios. Instead of using a two-stage
approach that involves image restoration and crowd counting modules, our model
learns effective features and adaptive queries to account for large appearance
variations. With these weather queries, the proposed model can learn the
weather information according to the degradation of the input image and
optimize with the crowd counting module simultaneously. Experimental results
show that the proposed algorithm is effective in counting crowds under
different weather types on benchmark datasets. The source code and trained
models will be made available to the public.
