  In this article, we propose a simulated crowd counting dataset CrowdX, which
has a large scale, accurate labeling, parameterized realization, and high
fidelity. The experimental results of using this dataset as data enhancement
show that the performance of the proposed streamlined and efficient benchmark
network ESA-Net can be improved by 8.4\%. The other two classic heterogeneous
architectures MCNN and CSRNet pre-trained on CrowdX also show significant
performance improvements. Considering many influencing factors determine
performance, such as background, camera angle, human density, and resolution.
Although these factors are important, there is still a lack of research on how
they affect crowd counting. Thanks to the CrowdX dataset with rich annotation
information, we conduct a large number of data-driven comparative experiments
to analyze these factors. Our research provides a reference for a deeper
understanding of the crowd counting problem and puts forward some useful
suggestions in the actual deployment of the algorithm.
