知搜-全球专利检索

  1. Login
  2. Sign Up

Search Results

31 records (took 0.019 seconds)

    TRAINING NEURAL NETWORKS FOR VEHICLE RE-IDENTIFICATION
    1.
    发明申请
    TRAINING NEURAL NETWORKS FOR VEHICLE RE-IDENTIFICATION 有权

    公开(公告)号：US20220392234A1

    公开(公告)日：2022-12-08

    申请号：US17890849

    申请日：2022-08-18

    Applicant: NVIDIA Corporation

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

    IPC: 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.

    TRIGGER-RESPONSIVE CLIP EXTRACTION BASED ON REMOTE ANALYSIS
    2.
    发明申请
    TRIGGER-RESPONSIVE CLIP EXTRACTION BASED ON REMOTE ANALYSIS 有权

    公开(公告)号：US20220165304A1

    公开(公告)日：2022-05-26

    申请号：US17103715

    申请日：2020-11-24

    Applicant: NVIDIA Corporation

    Inventor： Milind Ramesh Naphade ,  Parthasarathy Sriram ,  Shuo Wang

    IPC: G11B20/10 ,  H04N5/765 ,  G06N3/04

    Abstract: Intelligent Video Analytics system may be implemented using a distributed computing architecture with edge and remote devices, where the edge devices analyze the video stream and transmit detection data corresponding to time segments to the remote device. The detection data may identify an object (e.g., vehicle, pedestrian, etc.) in the video stream. The remote device analyzes the detection data received from one or more edge devices and generates extraction triggers that are transmitted to the one or more edge devices. When an edge device receives an extraction trigger, the edge device extracts a clip from the video stream and stores the clip to persistent storage. The remote device may then retrieve the clip. The edge devices may perform simple identification operations while the remote device implements complex algorithms to detect events, benefitting from a larger context than is available to the individual edge devices.

    TRAINING NEURAL NETWORKS FOR VEHICLE RE-IDENTIFICATION
    4.
    发明申请
    TRAINING NEURAL NETWORKS FOR VEHICLE RE-IDENTIFICATION 审中-公开

    公开(公告)号：US20200097742A1

    公开(公告)日：2020-03-26

    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 ,  G06N3/04 ,  G06N3/08 ,  G06F17/18 ,  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.

    SMART AREA MONITORING WITH ARTIFICIAL INTELLIGENCE
    5.
    发明申请
    SMART AREA MONITORING WITH ARTIFICIAL INTELLIGENCE 审中-公开

    公开(公告)号：US20190294889A1

    公开(公告)日：2019-09-26

    申请号：US16365581

    申请日：2019-03-26

    Applicant: NVIDIA Corporation

    Inventor： Parthasarathy Sriram ,  Ratnesh Kumar ,  Farzin Aghdasi ,  Arman Toorians ,  Milind Naphade ,  Sujit Biswas ,  Vinay Kolar ,  Bhanu Pisupati ,  Aaron Bartholomew

    IPC: G06K9/00 ,  H04N5/247 ,  G06T7/292 ,  G06T7/70

    Abstract: The present disclosure provides various approaches for smart area monitoring suitable for parking garages or other areas. These approaches may include ROI-based occupancy detection to determine whether particular parking spots are occupied by leveraging image data from image sensors, such as cameras. These approaches may also include multi-sensor object tracking using multiple sensors that are distributed across an area that leverage both image data and spatial information regarding the area, to provide precise object tracking across the sensors. Further approaches relate to various architectures and configurations for smart area monitoring systems, as well as visualization and processing techniques. For example, as opposed to presenting video of an area captured by cameras, 3D renderings may be generated and played from metadata extracted from sensors around the area.

    TRAINING NEURAL NETWORKS FOR VEHICLE RE-IDENTIFICATION
    10.
    发明申请
    TRAINING NEURAL NETWORKS FOR VEHICLE RE-IDENTIFICATION 有权

    公开(公告)号：US20250022092A1

    公开(公告)日：2025-01-16

    申请号：US18825755

    申请日：2024-09-05

    Applicant: NVIDIA Corporation

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

    IPC: G06T1/20 ,  G06F17/18 ,  G06N3/045 ,  G06N3/047 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82 ,  G06V20/52 ,  G06V20/58

    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.

Patent Agency Ranking