公开(公告)号：US20230259594A1

公开(公告)日：2023-08-17

申请号：US17670183

申请日：2022-02-11

Applicant: QUALCOMM Incorporated

Inventor： Dashan GAO ,  Lei WANG ,  Ning BI ,  Mithun Kumar RANGANATH ,  Chun-Ting HUANG ,  Scott RUSNAK ,  David NEAL

IPC: G06F21/32 ,  G06V40/16 ,  H04L9/32

CPC classification number: G06F21/32 ,  G06V40/16 ,  H04L9/3231 ,  H04L9/3236 ,  G06F2221/2141

Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media to implement a heterogenous biometric authentication process in a control system. For example, a method may include detecting the presence of a first person at a first time period and in an area associated with a function controlled by a control system. The method may include transmitting an authentication request to a first device detected by the control system, and receiving an authentication response from the first device. The authentication response includes information related to a biometric authentication performed at the first device. The method may further include authenticating the first person in the control system based on the information related to the biometric authentication. The method may then perform the function based on the authentication.

公开(公告)号：US20190114804A1

公开(公告)日：2019-04-18

申请号：US15966396

申请日：2018-04-30

Applicant: QUALCOMM Incorporated

Inventor： Sairam SUNDARESAN ,  Mithun Kumar RANGANATH ,  Matthew FISCHLER ,  Ning BI

IPC: G06T7/73 ,  G06K9/66 ,  G06K9/62 ,  G06K9/00

Abstract: Techniques and systems are provided for tracking objects in one or more images. For example, a trained network can be applied to a first image of a sequence of images to detect one or more objects in the first image. The trained network can be applied to less than all images of the sequence of images. A second image of the sequence of images and a detection result from application of the trained network to the first image are obtained. The detection result includes the detected one or more objects from the first image. A first object tracker can be applied to the second image using the detection result from application of the trained network to the first image. Applying the first object tracker can include adjusting one or more locations of one or more bounding boxes associated with the detected one or more objects in the second image to track the detected one or more objects in the second image. A second object tracker can also be applied to the second image to track at least one object of the detected one or more objects in the second image. The second object tracker is applied to more images of the sequence of images than the trained network and the first object tracker. Object tracking can be performed for the second image based on application of the first object tracker and the second object tracker.