  Cross-domain pedestrian detection aims to generalize pedestrian detectors
from one label-rich domain to another label-scarce domain, which is crucial for
various real-world applications. Most recent works focus on domain alignment to
train domain-adaptive detectors either at the instance level or image level.
From a practical point of view, one-stage detectors are faster. Therefore, we
concentrate on designing a cross-domain algorithm for rapid one-stage detectors
that lacks instance-level proposals and can only perform image-level feature
alignment. However, pure image-level feature alignment causes the
foreground-background misalignment issue to arise, i.e., the foreground
features in the source domain image are falsely aligned with background
features in the target domain image. To address this issue, we systematically
analyze the importance of foreground and background in image-level cross-domain
alignment, and learn that background plays a more critical role in image-level
cross-domain alignment. Therefore, we focus on cross-domain background feature
alignment while minimizing the influence of foreground features on the
cross-domain alignment stage. This paper proposes a novel framework, namely,
background-focused distribution alignment (BFDA), to train domain adaptive
onestage pedestrian detectors. Specifically, BFDA first decouples the
background features from the whole image feature maps and then aligns them via
a novel long-short-range discriminator.
