  Recently, counting the number of people for crowd scenes is a hot topic
because of its widespread applications (e.g. video surveillance, public
security). It is a difficult task in the wild: changeable environment,
large-range number of people cause the current methods can not work well. In
addition, due to the scarce data, many methods suffer from over-fitting to a
different extent. To remedy the above two problems, firstly, we develop a data
collector and labeler, which can generate the synthetic crowd scenes and
simultaneously annotate them without any manpower. Based on it, we build a
large-scale, diverse synthetic dataset. Secondly, we propose two schemes that
exploit the synthetic data to boost the performance of crowd counting in the
wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using
the real data, which significantly prompts the model's performance on real
data; 2) propose a crowd counting method via domain adaptation, which can free
humans from heavy data annotations. Extensive experiments show that the first
method achieves the state-of-the-art performance on four real datasets, and the
second outperforms our baselines. The dataset and source code are available at
https://gjy3035.github.io/GCC-CL/.
