  Accurately estimating the number of objects in a single image is a
challenging yet meaningful task and has been applied in many applications such
as urban planning and public safety. In the various object counting tasks,
crowd counting is particularly prominent due to its specific significance to
social security and development. Fortunately, the development of the techniques
for crowd counting can be generalized to other related fields such as vehicle
counting and environment survey, if without taking their characteristics into
account. Therefore, many researchers are devoting to crowd counting, and many
excellent works of literature and works have spurted out. In these works, they
are must be helpful for the development of crowd counting. However, the
question we should consider is why they are effective for this task. Limited by
the cost of time and energy, we cannot analyze all the algorithms. In this
paper, we have surveyed over 220 works to comprehensively and systematically
study the crowd counting models, mainly CNN-based density map estimation
methods. Finally, according to the evaluation metrics, we select the top three
performers on their crowd counting datasets and analyze their merits and
drawbacks. Through our analysis, we expect to make reasonable inference and
prediction for the future development of crowd counting, and meanwhile, it can
also provide feasible solutions for the problem of object counting in other
fields. We provide the density maps and prediction results of some mainstream
algorithm in the validation set of NWPU dataset for comparison and testing.
Meanwhile, density map generation and evaluation tools are also provided. All
the codes and evaluation results are made publicly available at
https://github.com/gaoguangshuai/survey-for-crowd-counting.
