  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.
