Towards Discriminative Semantic Relationship for Fine-grained Crowd Counting

Shiqi Ren, Chao Zhu, Mengyin Liu, Xu-Cheng Yin

Abstract: As an extended task of crowd counting, fine-grained crowd counting aims to estimate the number of people in each semantic category instead of the whole in an image, and faces challenges including 1) inter-category crowd appearance similarity, 2) intra-category crowd appearance variations, and 3) frequent scene changes. In this paper, we propose a new fine-grained crowd counting approach named DSR to tackle these challenges by modeling Discriminative Semantic Relationship, which consists of two key components: Word Vector Module (WVM) and Adaptive Kernel Module (AKM). The WVM introduces more explicit semantic relationship information to better distinguish people of different semantic groups with similar appearance. The AKM dynamically adjusts kernel weights according to the features from different crowd appearance and scenes. The proposed DSR achieves superior results over state-of-the-art on the standard dataset. Our approach can serve as a new solid baseline and facilitate future research for the task of fine-grained crowd counting.