CNN-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory Signal

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.