Crowd Detection via Point Localization with Diffusion Models

Crowd localization is a fundamental problem in understanding a crowded scene as it provides salient information such as location, density, and count about the crowd. However, the state-of-the-art face detection methods incorporate bounding box annotations, face landmark coordinates, and other structural ground truth information, which is expensive to prepare compared to the point annotation of faces. On the other hand, the state-of-the-art crowd localization methods are based on regressing the point coordinates and classification scores, which hinders the ability to learn the distributions of point coordinates and confidence scores for a given crowd dataset. The learned distribution allows us to infer new insights for a given crowd image, which in turn helps improve the confidence scores for positive sample detection. We propose solving the crowd localization task as a generative method using recently proposed denoising diffusion probabilistic models. Furthermore, we leverage the stochastic nature of the generative model by using multiple realizations of localization results for a given crowd image to improve crowd detection performance. We conduct extensive experiments on publicly available datasets to validate the effectiveness of our method. The proposed method improves localization performance metrics: precision, recall, and F1-measure by at least 2% for UCF-QNRF and ShanghaiTech A datasets and achieves state-of-the-art counting performance across five public crowd counting datasets.