Global Context Instructive Network for Extreme Crowd Counting

Abstract

Crowd counting has gained popularity due to wide applications, such as intelligent security, and urban planning. However, scale variation and perspective distortion make it a challenging task. Most existing works focus on multi-scale feature extraction to address the challenge of scale variation and perspective distortion. In this paper, we propose a novel Global Context Instructive Network (GCINet), which devotes to making full use of extracted features and obtaining precise counts. The main contributions are four folds. First, we construct a three-column Feature Processor to generate features with different scales. Second, an Instructive Module is proposed to introduce global context which is the substance for generating adaptive features. Based on global context, the three-column Feature Processor constitutes an adaptive feature generator. Third, a novel loss function which integrates Euclidean distance and spatial correlation is proposed to enhance the spatial correlation and consistency between pixels. We no longer regard a pixel as an independent point in the calculation, but consider the neighborhood space of the pixel to achieve complementary effects. Finally, we conduct experiments on ShanghaiTech dataset, UCF_CC_50 dataset, UCF_QNRF dataset and UCSD dataset which show that our approach achieves state-of-the-art performance.