PromptHC: Multi-attention prompt guided haze-weather crowd counting

Abstract

Existing crowd counting methods encounter the challenge of degraded performance in hazy weather due to the blurring of pedestrian outlines. However, current hazy-weather crowd counting methods primarily focus on extracting crowd features, often neglecting the varying degrees of distortion in pedestrian outlines caused by inhomogeneous haze distribution. To this end, this paper develops a multi-attention prompt guided method for hazy-weather crowd counting, termed PromptHC. Specially, to explore the relationship between varying haze concentrations and the pedestrian outlines, a multi-attention dynamically adjustable prompt module is designed to provide crucial prompts about crowd features in hazy weather. Meanwhile, to further enhance the anti-interference capability of the model in hazy weather, a progressive guidance module is incorporated, which effectively reduces interference from different haze concentrations by guiding the learning of crowd attention. Furthermore, a global context-enhanced crowd feature extraction module is designed to capture precise global information. A series of ablation studies verify the actual effectiveness of each core component of the PromptHC. In addition, we conduct a performance comparison with the current mainstream methods on two hazy-weather datasets. Experimental results show the feasibility and superiority of the PromptHC for the hazy-weather crowd counting task.