Inverse Attention Guided Deep Crowd Counting Network
Vishwanath A. Sindagi, Vishal M. Patel · 2019
In this paper, we address the challenging problem of crowd counting in congested scenes. Specifically, we present Inverse Attention Guided Deep Crowd Counting Network (IA-DCCN) that efficiently infuses segmentation information through an inverse attention mechanism into the counting network, resulting in significant improvements. The proposed method, which is based on VGG-16, is a single-step training framework and is simple to implement. The use of segmentation information does not require additional annotation efforts. We demonstrate the significance of segmentation guided inverse attention through a detailed analysis and ablation study. Furthermore, the proposed method is evaluated on three challenging crowd counting datasets and is shown to achieve significant improvements over several recent methods.