  Crowd counting is an effective tool for situational awareness in public
places. Automated crowd counting using images and videos is an interesting yet
challenging problem that has gained significant attention in computer vision.
Over the past few years, various deep learning methods have been developed to
achieve state-of-the-art performance. The methods evolved over time vary in
many aspects such as model architecture, input pipeline, learning paradigm,
computational complexity, and accuracy gains etc. In this paper, we present a
systematic and comprehensive review of the most significant contributions in
the area of crowd counting. Although few surveys exist on the topic, our survey
is most up-to date and different in several aspects. First, it provides a more
meaningful categorization of the most significant contributions by model
architectures, learning methods (i.e., loss functions), and evaluation methods
(i.e., evaluation metrics). We chose prominent and distinct works and excluded
similar works. We also sort the well-known crowd counting models by their
performance over benchmark datasets. We believe that this survey can be a good
resource for novice researchers to understand the progressive developments and
contributions over time and the current state-of-the-art.
