公开(公告)号：US20250005956A1

公开(公告)日：2025-01-02

申请号：US18828950

申请日：2024-09-09

Applicant: NVIDIA Corporation

Inventor： Parthasarathy Sriram ,  Fnu Ratnesh Kumar ,  Anil Ubale ,  Farzin Aghdasi ,  Yan Zhai ,  Subhashree Radhakrishnan

IPC: G06V40/10 ,  G06N3/045 ,  G06N3/08 ,  G06T7/246 ,  G06V10/26 ,  G06V10/44 ,  G06V10/82 ,  G06V20/52

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.

公开(公告)号：US11455807B2

公开(公告)日：2022-09-27

申请号：US16577716

申请日：2019-09-20

Applicant: NVIDIA Corporation

Inventor： Fnu Ratnesh Kumar ,  Farzin Aghdasi ,  Parthasarathy Sriram ,  Edwin Weill

IPC: G06K9/00 ,  G06V20/58 ,  G06F17/18 ,  G06N3/04 ,  G06N3/08 ,  G06T1/20

Abstract: In various examples, a neural network may be trained for use in vehicle re-identification tasks—e.g., matching appearances and classifications of vehicles across frames—in a camera network. The neural network may be trained to learn an embedding space such that embeddings corresponding to vehicles of the same identify are projected closer to one another within the embedding space, as compared to vehicles representing different identities. To accurately and efficiently learn the embedding space, the neural network may be trained using a contrastive loss function or a triplet loss function. In addition, to further improve accuracy and efficiency, a sampling technique—referred to herein as batch sample—may be used to identify embeddings, during training, that are most meaningful for updating parameters of the neural network.