Jointly attention network for crowd counting

Abstract

Nowadays, crowd counting has shown great practical value in public safety and related fields. Most leading algorithms exploit CNN to generate density maps and have improved the estimation accuracy. However, the counting models still suffer from the challenge of huge scale variations. In order to mitigate this issue, we propose a novel approach named Jointly Attention Network (JANet) for Crowd Counting. It is composed of two major schemes: the Multi-order Scale Attention (MSA) module and the Multi-pooling Relational Channel Attention (MRCA) module. The MSA module explores meaningful high-order statistics and helps the backbone network obtain more discriminative features with rich scale information in an explicit manner. The MRCA module compactly represents the global scope relations and accounts the interdependence among all channel-wise nodes, which is complementary to MSA module. Meanwhile, the Distributed Combinatorial Loss (DCL) is designed to achieve the distributed supervision on intermediate layers at each level. Finally, we conduct extensive studies on multiple crowd counting datasets, the ShanghaiTech, the UCF-QNRF, the JHU-CROWD++, the NWPU-Crowd. The experimental results indicate that our proposed method has achieved superior performance.