  With the development of deep neural networks, the performance of crowd
counting and pixel-wise density estimation are continually being refreshed.
Despite this, there are still two challenging problems in this field: 1)
current supervised learning needs a large amount of training data, but
collecting and annotating them is difficult; 2) existing methods can not
generalize well to the unseen domain. A recently released synthetic crowd
dataset alleviates these two problems. However, the domain gap between the
real-world data and synthetic images decreases the models' performance. To
reduce the gap, in this paper, we propose a domain-adaptation-style crowd
counting method, which can effectively adapt the model from synthetic data to
the specific real-world scenes. It consists of Multi-level Featureaware
Adaptation (MFA) and Structured Density map Alignment (SDA). To be specific,
MFA boosts the model to extract domain-invariant features from multiple layers.
SDA guarantees the network outputs fine density maps with a reasonable
distribution on the real domain. Finally, we evaluate the proposed method on
four mainstream surveillance crowd datasets, Shanghai Tech Part B,
WorldExpo'10, Mall and UCSD. Extensive experiments evidence that our approach
outperforms the state-of-the-art methods for the same cross-domain counting
problem.
