  The aim of re-identification is to match objects in surveillance cameras with
different viewpoints. Although ReID is developing at a considerably rapid pace,
there is currently no processing method for the ReID task in multiple
scenarios. However, such processing method is required in real life scenarios,
such as those involving security. In the present study, a new ReID scenario was
explored, which differs in terms of perspective, background, and pose(walking
or cycling). Obviously, ordinary ReID processing methods cannot effectively
handle such a scenario, with the introduction of image datasets being the
optimal solution, in addition to being considerably expensive.
  To solve the aforementioned problem, a simple and effective method to
generate images in several new scenarios was proposed, which is names the Copy
and Paste method based on Pose(CPP). The CPP method is based on key point
detection, using copy as paste, to composite a new semantic image dataset in
two different semantic image datasets. As an example, pedestrains and bicycles
can be used to generate several images that show the same person riding on
different bicycles. The CPP method is suitable for ReID tasks in new scenarios
and outperforms the traditional methods when applied to the original datasets
in original ReID tasks. To be specific, the CPP method can also perform better
in terms of generalization for third-party public dataset. The Code and
datasets composited by the CPP method will be available in the future.
