earticle

영어

Crowd counting, an essential task with applications in crowd management, urban planning, and public safety, continues to pose challenges. While foundational approaches have laid the groundwork, persistent issues include limited generalization and sensitivity to crowd density variations. In this paper, we present a spatial attention-based crowd-counting architecture. The aim is to enhance the model’s adaptability to diverse scenes and improve crowd-counting accuracy. Experimental results demonstrate a notable decrease in training time and a performance improvement. This marks a significant stride in refining crowd-counting methodologies and paving the way for future advancements in the field. Building upon this foundation, our proposed methodology introduces a novel approach to address the challenges inherent in crowd counting. By incorporating spatial attention mechanisms into a multi-column convolutional neural network (CNN), we aim to further enhance the efficiency and accuracy of crowd counting in complex scenarios. Our methodology leverages the inherent capabilities of spatial attention to dynamically adjust focus within crowd scenes, enabling better adaptation and more precise counting in varying conditions.