Copy and Paste method based on Pose for Re-identification

TL;DR

This paper introduces the Copy and Paste based on Pose (CPP) method, which generates new scenario images for re-identification tasks using key point detection, improving performance in diverse viewpoints and backgrounds without expensive dataset collection.

Contribution

The novel CPP method leverages pose-based key point detection to synthesize new images, enhancing ReID performance across varied scenarios and datasets.

Findings

CPP outperforms traditional methods on original datasets.

CPP improves generalization on third-party public datasets.

Generated datasets facilitate ReID in complex scenarios.

Abstract

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.