  Drones shooting can be applied in dynamic traffic monitoring, object
detecting and tracking, and other vision tasks. The variability of the shooting
location adds some intractable challenges to these missions, such as varying
scale, unstable exposure, and scene migration. In this paper, we strive to
tackle the above challenges and automatically understand the crowd from the
visual data collected from drones. First, to alleviate the background noise
generated in cross-scene testing, a double-stream crowd counting model is
proposed, which extracts optical flow and frame difference information as an
additional branch. Besides, to improve the model's generalization ability at
different scales and time, we randomly combine a variety of data transformation
methods to simulate some unseen environments. To tackle the crowd density
estimation problem under extreme dark environments, we introduce synthetic data
generated by game Grand Theft Auto V(GTAV). Experiment results show the
effectiveness of the virtual data. Our method wins the challenge with a mean
absolute error (MAE) of 12.70. Moreover, a comprehensive ablation study is
conducted to explore each component's contribution.
