Inverse Attention Guided Deep Crowd Counting Network
This addresses the problem of accurate crowd estimation in dense environments for applications like surveillance and urban planning, representing an incremental improvement through a novel attention mechanism.
The paper tackles crowd counting in congested scenes by proposing an inverse attention mechanism that infuses segmentation information into a VGG-16-based network, achieving significant improvements over recent methods on three challenging datasets.
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 results in minimal computational overhead and does not require any additional annotations. 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.