DDRANet: A Dynamic Density-Region-Aware Network for Crowd Counting

In recent years, crowd counting has garnered increasing attention due to its wide range of societal applications. However, the vast differences in crowd density distributions across various areas within a scene make it difficult for existing crowd counting methods to achieve satisfactory performance. To address this issue, this letter introduces a novel method called Dynamic Density-Region-Aware Network (DDRANet). First, a Density-Aware Module (DAM) is designed to detect density variations and dynamically segment the image into multiple density regions. Subsequently, the Region Information Encoder (RIE) transforms the obtained density region map into a latent space to enhance the information of density regions. Then, through a specially designed Region Attention Block (RAB), the encoded density region map guides the model to focus on different density regions separately, enabling the model to adapt to the density variations of the crowd within the image. Furthermore, we combine Gaussian kernel function and Density Peak Clustering to automatically generate ground truth of density region maps, supervising the optimization of DAM. Extensive experimental results on three datasets demonstrate DDRANet's superior performance over other state-of-the-art methods.