Crowd Counting and Localization in Haze and Rain

Adverse weather conditions such as haze and fog often significantly reduce the performance of crowd counting models. An intuitive solution is to preprocess degraded images by applying image restoration techniques prior to crowd counting. However, this solution introduces additional computational com...

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Bibliographic Details
Main Authors: Yuan, Weijun, Li, Zhan, Li, Xiaohan, Fang, Liangda, Zhang, Qingfeng, Qiu, Zhixiang
Format: Conference Proceeding
Language:English
Subjects:
adverse weather
Computational modeling
crowd counting and localization
image dehazing
Image restoration
joint learning
Knowledge engineering
Location awareness
Noise
Rain
Snow
Online Access:Request full text
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Summary:Adverse weather conditions such as haze and fog often significantly reduce the performance of crowd counting models. An intuitive solution is to preprocess degraded images by applying image restoration techniques prior to crowd counting. However, this solution introduces additional computational complexity and may produce restored images with noise and artifacts that is harmful to the subsequent crowd counting task. To mitigate the two issues, we integrate an image restoration module (IRM) into a unified framework to propose an effective network for crowd counting and localization in haze and rain. The lightweight IRM is designed to guide the network to learn haze-aware knowledge in feature space, which is removed in the inference phase without increasing the computational cost. In addition, two new datasets are constructed to evaluate the crowd counting methods in haze and rain. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed method. The code is available at https://github.com/lizhangray/Dehaze-P2PNet.
ISSN:1945-788X
DOI:10.1109/ICME57554.2024.10687579