Prototype-Guided Dual-Transformer Reasoning for Video Individual Counting

Video Individual Counting (VIC), which focuses on accurately tallying the total number of individuals in a video without duplication, is crucial for urban public space management and densely-populated areas planning. Existing methods suffer from limitations in terms of expensive manual annotation, and the efficiency of location or detection algorithms. In this work, we contribute a novel Prototype-guided Dual-Transformer Reasoning framework, termed PDTR, which takes both similarity and difference of adjacent frames into account to achieve accurate counting in an end-to-end regression manner. Specifically, we first design a multi-receptive field feature fusion module to acquire initial comprehensive representations. Subsequently, the dynamic prototype generation module memorizes consistent representations of similar information to generate prototypes. Additionally, to further dig out the shared and private features from different frames, a prototype cross-guided decoder and a privacy-decoupling module are designed. Extensive experiments conducted on two existing VIC datasets, consistently demonstrate the superiority of PDTR over state-of-the-art baselines.