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.

翻译：人群计数因其在图像理解中的广泛应用，近年来在计算机视觉领域备受关注。大量方法已被提出并在实际任务中取得了先进性能。然而，现有方法在雾、雨、雪等恶劣天气下表现不佳，因为此类场景中拥挤人群的视觉外观与典型数据集中清晰天气下的图像存在显著差异。本文提出一种在恶劣天气场景下实现鲁棒人群计数的方法。不同于采用图像恢复与人群计数模块的两阶段方法，我们的模型通过学习有效特征和自适应查询来处理大幅度的外观变化。借助这些天气查询，所提模型能够根据输入图像的退化程度学习天气信息，并与人群计数模块同步优化。实验结果表明，所提算法在基准数据集的不同天气类型下均能有效进行人群计数。源代码与训练模型将向公众开放。