  Single image crowd counting is a challenging computer vision problem with
wide applications in public safety, city planning, traffic management, etc.
This survey is to provide a comprehensive summary of recent advanced crowd
counting techniques based on Convolutional Neural Network (CNN) via density map
estimation. 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 conclude the survey with some future directions.
