  In person search, we aim to localize a query person from one scene in other
gallery scenes. The cost of this search operation is dependent on the number of
gallery scenes, making it beneficial to reduce the pool of likely scenes. We
describe and demonstrate the Gallery Filter Network (GFN), a novel module which
can efficiently discard gallery scenes from the search process, and benefit
scoring for persons detected in remaining scenes. We show that the GFN is
robust under a range of different conditions by testing on different retrieval
sets, including cross-camera, occluded, and low-resolution scenarios. In
addition, we develop the base SeqNeXt person search model, which improves and
simplifies the original SeqNet model. We show that the SeqNeXt+GFN combination
yields significant performance gains over other state-of-the-art methods on the
standard PRW and CUHK-SYSU person search datasets. To aid experimentation for
this and other models, we provide standardized tooling for the data processing
and evaluation pipeline typically used for person search research.
