  As a special type of object detection, pedestrian detection in generic scenes
has made a significant progress trained with large amounts of labeled training
data manually. While the models trained with generic dataset work bad when they
are directly used in specific scenes. With special viewpoints, flow light and
backgrounds, datasets from specific scenes are much different from the datasets
from generic scenes. In order to make the generic scene pedestrian detectors
work well in specific scenes, the labeled data from specific scenes are needed
to adapt the models to the specific scenes. While labeling the data manually
spends much time and money, especially for specific scenes, each time with a
new specific scene, large amounts of images must be labeled. What's more, the
labeling information is not so accurate in the pixels manually and different
people make different labeling information. In this paper, we propose an
ACP-based method, with augmented reality's help, we build the virtual world of
specific scenes, and make people walking in the virtual scenes where it is
possible for them to appear to solve this problem of lacking labeled data and
the results show that data from virtual world is helpful to adapt generic
pedestrian detectors to specific scenes.
