Region feature smoothness assumption for weakly semi‐supervised crowd counting

Abstract

AbstractCrowd counting is a hot issue in visual data processing. It also plays an important role in the field of video surveillance, social security, and traffic control. However, most of the existing crowd counting methods always adopt a mount of training data or point‐level annotation to learn the mapping relationships between images and density maps, which would cost much human labor. In this paper, we propose a new weakly semi‐supervised crowd counting method which uses less count‐level data for data training. In particular, we extend the classical smoothness assumption and design a many‐to‐many Region Feature Smoothness Assumption to deal with the uneven density distribution problem within crowd region. Further, we adopt hypergraph representation to explore the complex high‐order relationship for different crowd regions. Besides, we design a multi‐scale dynamic hypergraph convolutional module and hyperedge contrastive loss. Extensive experiments have been conducted on five public datasets. The experimental results show that the proposed method outperforms the state‐of‐the‐art ones.