  For pixel-level crowd understanding, it is time-consuming and laborious in
data collection and annotation. Some domain adaptation algorithms try to
liberate it by training models with synthetic data, and the results in some
recent works have proved the feasibility. However, we found that a mass of
estimation errors in the background areas impede the performance of the
existing methods. In this paper, we propose a domain adaptation method to
eliminate it. According to the semantic consistency, a similar distribution in
deep layer's features of the synthetic and real-world crowd area, we first
introduce a semantic extractor to effectively distinguish crowd and background
in high-level semantic information. Besides, to further enhance the adapted
model, we adopt adversarial learning to align features in the semantic space.
Experiments on three representative real datasets show that the proposed domain
adaptation scheme achieves the state-of-the-art for cross-domain counting
problems.
