公开(公告)号：US11367268B2

公开(公告)日：2022-06-21

申请号：US16998890

申请日：2020-08-20

Applicant: NVIDIA Corporation

Inventor： Xiaodong Yang ,  Yang Zou ,  Zhiding Yu ,  Jan Kautz

IPC: G06K9/44 ,  G06V10/34 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06V10/40 ,  G06V20/40 ,  G06V30/262

Abstract: Object re-identification refers to a process by which images that contain an object of interest are retrieved from a set of images captured using disparate cameras or in disparate environments. Object re-identification has many useful applications, particularly as it is applied to people (e.g. person tracking). Current re-identification processes rely on convolutional neural networks (CNNs) that learn re-identification for a particular object class from labeled training data specific to a certain domain (e.g. environment), but that do not apply well in other domains. The present disclosure provides cross-domain disentanglement of id-related and id-unrelated factors. In particular, the disentanglement is performed using a labeled image set and an unlabeled image set, respectively captured from different domains but for a same object class. The identification-related features may then be used to train a neural network to perform re-identification of objects in that object class from images captured from the second domain.

公开(公告)号：US20210064907A1

公开(公告)日：2021-03-04

申请号：US16998890

申请日：2020-08-20

Applicant: NVIDIA Corporation

Inventor： Xiaodong Yang ,  Yang Zou ,  Zhiding Yu ,  Jan Kautz

IPC: G06K9/44 ,  G06K9/46 ,  G06K9/62

Abstract: Object re-identification refers to a process by which images that contain an object of interest are retrieved from a set of images captured using disparate cameras or in disparate environments. Object re-identification has many useful applications, particularly as it is applied to people (e.g. person tracking). Current re-identification processes rely on convolutional neural networks (CNNs) that learn re-identification for a particular object class from labeled training data specific to a certain domain (e.g. environment), but that do not apply well in other domains. The present disclosure provides cross-domain disentanglement of id-related and id-unrelated factors. In particular, the disentanglement is performed using a labeled image set and an unlabeled image set, respectively captured from different domains but for a same object class. The identification-related features may then be used to train a neural network to perform re-identification of objects in that object class from images captured from the second domain.