A survey on deep learning-based single image crowd counting: Network design, loss function and supervisory signal

Abstract

Single image crowd counting is a challenging computer vision problem with wide applications in public safety, city planning, traffic management, etc. With the recent development of deep learning techniques, crowd counting has aroused much attention and achieved great success in recent years. This survey is to provide a comprehensive summary of recent advances on deep learning-based crowd counting techniques via density map estimation by systematically reviewing and summarizing more than 200 works in the area since 2015. Our goals are to provide an up-to-date review of recent approaches, and educate new researchers in this field the design principles and trade-offs. After presenting publicly available datasets and evaluation metrics, we review the recent advances with detailed comparisons on three major design modules for crowd counting: deep neural network designs, loss functions, and supervisory signals. We study and compare the approaches using the public datasets and evaluation metrics. We conclude the survey with some future directions.