公开(公告)号：US12254699B1

公开(公告)日：2025-03-18

申请号：US18823263

申请日：2024-09-03

Applicant: Samsara Inc.

Inventor： Shaurye Agarwal ,  Akshay Raj Dhamija ,  Howard Yu ,  Margaret Irene Finch ,  Jing Wang ,  Rohit Annigeri ,  Sharan Srinivasan ,  Yan Wang ,  Nathan Hurst

IPC: G06V20/56 ,  G06T3/60 ,  G06V10/44 ,  G06V10/764

Abstract: Methods, systems, and programs are presented for detecting impaired views in monitoring cameras. One method includes training a rotation classifier with unsupervised learning utilizing a first set of images. The rotation classifier is configured to receive an input image and generate a rotation feature embedding for the input image. In addition, the method includes training an impairment classifier with supervised learning utilizing a second set of images, impairment labels for each of the second set of images, and the rotation feature embedding, generated by the rotation classifier, for each of the second set of images. The method further includes accessing a vehicle image captured by a camera on a vehicle, and providing the vehicle image to the impairment classifier as input, and the impairment classifier outputs a camera impairment from a set of camera impairment categories. Further, the vehicle image and the camera impairment are presented on a user interface.

公开(公告)号：US12112548B1

公开(公告)日：2024-10-08

申请号：US18618182

申请日：2024-03-27

Applicant: Samsara Inc.

Inventor： Shaurye Agarwal ,  Akshay Raj Dhamija ,  Howard Yu ,  Margaret Irene Finch ,  Jing Wang ,  Rohit Annigeri ,  Sharan Srinivasan ,  Yan Wang ,  Nathan Hurst

IPC: G06V20/56 ,  G06T3/60 ,  G06V10/44 ,  G06V10/764

CPC classification number: G06V20/56 ,  G06T3/60 ,  G06V10/44 ,  G06V10/764

Abstract: Methods, systems, and programs are presented for detecting impaired views in monitoring cameras. One method includes training a rotation classifier with unsupervised learning utilizing a first set of images. The rotation classifier is configured to receive an input image and generate a rotation feature embedding for the input image. In addition, the method includes training an impairment classifier with supervised learning utilizing a second set of images, impairment labels for each of the second set of images, and the rotation feature embedding, generated by the rotation classifier, for each of the second set of images. The method further includes accessing a vehicle image captured by a camera on a vehicle, and providing the vehicle image to the impairment classifier as input, and the impairment classifier outputs a camera impairment from a set of camera impairment categories. Further, the vehicle image and the camera impairment are presented on a user interface.