Invention Grant
- Patent Title: Determining associations between objects and persons using machine learning models
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Application No.: US17556451Application Date: 2021-12-20
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Publication No.: US11741736B2Publication Date: 2023-08-29
- Inventor: Parthasarathy Sriram , Fnu Ratnesh Kumar , Anil Ubale , Farzin Aghdasi , Yan Zhai , Subhashree Radhakrishnan
- Applicant: NVIDIA Corporation
- Applicant Address: US CA Santa Clara
- Assignee: NVIDIA Corporation
- Current Assignee: NVIDIA Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Taylor English Duma L.L.P.
- Main IPC: G06V40/10
- IPC: G06V40/10 ; G06N3/08 ; G06T7/246 ; G06V10/26 ; G06V20/52 ; G06N3/045 ; G06V10/82 ; G06V10/44

Abstract:
In various examples, sensor data—such as masked sensor data—may be used as input to a machine learning model to determine a confidence for object to person associations. The masked sensor data may focus the machine learning model on particular regions of the image that correspond to persons, objects, or some combination thereof. In some embodiments, coordinates corresponding to persons, objects, or combinations thereof, in addition to area ratios between various regions of the image corresponding to the persons, objects, or combinations thereof, may be used to further aid the machine learning model in focusing on important regions of the image for determining the object to person associations.
Public/Granted literature
- US20220114800A1 DETERMINING ASSOCIATIONS BETWEEN OBJECTS AND PERSONS USING MACHINE LEARNING MODELS Public/Granted day:2022-04-14
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