  In this work, we construct a large-scale dataset for Ground-to-Aerial Person
Search, named G2APS, which contains 31,770 images of 260,559 annotated bounding
boxes for 2,644 identities appearing in both of the UAVs and ground
surveillance cameras. To our knowledge, this is the first dataset for
cross-platform intelligent surveillance applications, where the UAVs could work
as a powerful complement for the ground surveillance cameras. To more
realistically simulate the actual cross-platform Ground-to-Aerial surveillance
scenarios, the surveillance cameras are fixed about 2 meters above the ground,
while the UAVs capture videos of persons at different location, with a variety
of view-angles, flight attitudes and flight modes. Therefore, the dataset has
the following unique characteristics: 1) drastic view-angle changes between
query and gallery person images from cross-platform cameras; 2) diverse
resolutions, poses and views of the person images under 9 rich real-world
scenarios. On basis of the G2APS benchmark dataset, we demonstrate detailed
analysis about current two-step and end-to-end person search methods, and
further propose a simple yet effective knowledge distillation scheme on the
head of the ReID network, which achieves state-of-the-art performances on both
of the G2APS and the previous two public person search datasets, i.e., PRW and
CUHK-SYSU. The dataset and source code available on
\url{https://github.com/yqc123456/HKD_for_person_search}.
