Abstract
Re-identification(ReID) aims at matching objects in surveillance cameras with different viewpoints. It's developing very fast, but there is no processing method for the ReID task in multiple scenarios at this stage. However, this dose happen all the time in real life, such as the security scenarios. This paper explores a new scenario of Re-identification, which differs in perspective, background, and pose(walking or cycling). Obviously, ordinary ReID processing methods cannot handle this scenario well. As we all konw, the best way to deal with that it is to introduce image datasets in this scanario, But this one is very expensive. To solve this problem, this paper proposes a simple and effective way to generate images in some new scenario, which is named Copy and Paste method based on Pose(CPP). The CPP is a method based on key point detection, using copy and paste, to composite a new semantic image dataset in two different semantic image datasets. Such as, we can use pedestrians and bicycles to generate some images that shows the same person rides on different bicycles. The CPP is suitable for ReID tasks in new scenarios and it outperforms state-of-the-art on the original datasets in original ReID tasks. Specifically, it can also have better generalization performance for third-party public datasets. Code and Datasets which composited by the CPP will be available in the future.
Abstract (translated)
URL
https://arxiv.org/abs/2107.10479
