Abstract: To address the challenges of crowd counting in dense scenes, such as complex backgrounds and scale variations, we propose a weakly supervised crowd counting model for dense scenes, named GLCrowd, which integrates global and local attention mechanisms. First, we design a local attention module combined with deep convolution to enhance local features through context weights while leveraging feature weight sharing to capture high-frequency local information. Second, the Vision Transformer (ViT) self-attention mechanism is used to capture low-frequency global information. Finally, the global and local attention mechanisms are effectively fused, and counting is accomplished through a regression token. The model was tested on the Shanghai Tech Part A, Shanghai Tech Part B, UCF-QNRF, and UCF_CC_50 datasets, achieving MAE values of 64.884, 8.958, 95.523, and 209.660, and MSE values of 104.411, 16.202, 173.453, and 282.217, respectively. The results demonstrate that the proposed GLCrowd model exhibits strong performance in crowd counting within dense scenes.