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    Methods and apparatus to calibrate a multiple camera system based on a human pose
    23.
    发明授权
    Methods and apparatus to calibrate a multiple camera system based on a human pose 有权

    公开(公告)号：US12039756B2

    公开(公告)日：2024-07-16

    申请号：US17132950

    申请日：2020-12-23

    Applicant: Intel Corporation

    Inventor： Julio Cesar Zamora Esquivel ,  David Israel Gonzalez Aguirre

    IPC: H04N5/04 ,  G06T7/73 ,  G06T7/80 ,  H04N23/90

    CPC classification number: G06T7/80 ,  G06T7/74 ,  H04N5/04 ,  H04N23/90 ,  G06T2207/30196 ,  G06T2207/30244

    Abstract: Methods and apparatus to calibrate a multicamera system based on a human pose are disclosed. An example apparatus includes an object identifier to identify a first set of coordinates defining first locations of anatomical points of a human in a first image captured by a first camera and identify a second set of coordinates defining second locations of the anatomical points in a second image captured by a second camera. The apparatus includes a pose detector to detect, based on at least one of the first or second sets of coordinates, when the human is in a particular pose. The apparatus includes a transformation calculator to, in response to detection of the human in the particular pose, calculate a relative transformation between the first camera and the second camera based on a first subset of the first set of coordinates and a second subset of the second set of coordinates.

    OCCUPANCY MAPPING BASED ON GEOMETRIC ENTITIES WITH HIERARCHICAL RELATIONSHIPS
    24.
    发明公开
    OCCUPANCY MAPPING BASED ON GEOMETRIC ENTITIES WITH HIERARCHICAL RELATIONSHIPS 审中-公开

    公开(公告)号：US20230259665A1

    公开(公告)日：2023-08-17

    申请号：US18303782

    申请日：2023-04-20

    Applicant: Intel Corporation

    Inventor： Leobardo Campos Macias ,  Rafael De La Guardia Gonzalez ,  David Gonzalez Aguirre ,  Javier Felip Leon ,  Julio Cesar Zamora Esquivel

    IPC: G06F30/10 ,  G06F30/27

    CPC classification number: G06F30/10 ,  G06F30/27

    Abstract: A system can map occupancy of objects. The system may generate an occupancy representation of an object based on a point cloud of the object. The system may generate first geometric entities based on the point cloud. Each first geometric entity contains one or more points in the point cloud. The system may also generate one or more second geometric entities, each of which contains one or more first geometric entities. The occupancy representation of the object includes the one or more second geometric entities and the plurality of first geometric entities. The occupancy representation may have a hierarchical structure where the first geometric entities may be on a lower level than the one or more second geometric entities. The system can also detect collision of the object with another object by using the occupancy representation of the object and an occupancy representation of the other object.

    SYSTEM AND METHOD OF USING FRACTIONAL ADAPTIVE LINEAR UNIT AS ACTIVATION IN ARTIFACIAL NEURAL NETWORK
    26.
    发明申请
    SYSTEM AND METHOD OF USING FRACTIONAL ADAPTIVE LINEAR UNIT AS ACTIVATION IN ARTIFACIAL NEURAL NETWORK 有权

    公开(公告)号：US20220101138A1

    公开(公告)日：2022-03-31

    申请号：US17548692

    申请日：2021-12-13

    Applicant: Intel Corporation

    Inventor： Anthony Daniel Rhodes ,  Julio Cesar Zamora Esquivel ,  Lama Nachman

    IPC: G06N3/08 ,  G06N3/04

    Abstract: An apparatus is provided for deep learning. The apparatus accesses a neural network including an input layer, hidden layers, and an output layer. The apparatus adds an activation function to one or more of the hidden layers of the hidden layers and output layer. The activation function includes a tunable parameter, the value of which can be adjusted during the training of the neural network. The apparatus trains the neural network by inputting training samples into the neural network and determining internal parameters of the neural network based on the training samples. Determining the internal parameters includes determining a value of the tunable parameter based on the training samples. The apparatus may determine two different values of the tunable parameter for two different layers. The activation function may include another tunable parameter. The apparatus can determine a value for the other tunable parameter during the training of the neural network.

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