Crowd counting in smart city via lightweight Ghost Attention Pyramid Network

Abstract

Crowd counting targets for determining the number of pedestrians in an image, which is of crucial importance for smart city construction. The problem of scale variation is an ingrained and drastic challenge in crowd counting, and it severely degrades the performance of counting. To address this problem, many powerful models with complex network structures and tricks are built, but the constrained resources of embedded systems prevent the direct deployment of these models into an edge device. Thus, it is on high demand to design favourable lightweight models that require fewer parameters and a fast inference speed, while maintaining competitive counting performance. To this aim, we devise a lightweight network, termed as Ghost Attention Pyramid Network (GAPNet). Specifically, a lightweight GhostNet is adopted as the backbone to encode low-level features. Subsequently, a zero-parameter channel attention module is designed to select the discriminative crowd region efficiently. In addition, an efficient pyramid fusion module is built with a four-branch architecture to obtain multiscale hierarchy representations while reducing the parameters. Finally, a decoder generates the prediction by exploiting a series of transposed convolution blocks. Extensive experiments on crowd counting benchmarks have proved the superiority of the GAPNet in both accuracy and efficiency.