公开(公告)号：US20240312055A1

公开(公告)日：2024-09-19

申请号：US18569996

申请日：2021-12-10

Applicant: INTEL CORPORATION

Inventor： Shandong WANG ,  Yurong CHEN ,  Ming LU ,  Li XU ,  Anbang YAO

IPC: G06T7/73 ,  G06T7/80

CPC classification number: G06T7/74 ,  G06T7/80 ,  G06T2207/20084 ,  G06T2207/30196 ,  G06T2207/30221

Abstract: This disclosure describes systems, methods, and devices related to real-time multi-person three-dimensional pose tracking using a single camera. A method may include receiving, by a device, two-dimensional image data from a camera, the two-dimensional image data representing a first person and a second person; generating, based on the two-dimensional image data, two-dimensional positions of body parts represented by the first person; generating, using a deep neural network, based on the two-dimensional positions, a three-dimensional pose regression of the body parts represented by the first person; identifying, based on the two-dimensional positions and the three-dimensional pose regression, contact between a ground plane and a foot of the first person; generating an absolute three-dimensional position of the contact between the ground plane and the foot of the first person; generating, based on the absolute three-dimensional position, a three-dimensional pose of the body parts represented by the first person.

公开(公告)号：US20230410496A1

公开(公告)日：2023-12-21

申请号：US18252164

申请日：2020-12-23

Applicant: Intel Corporation

Inventor： Anbang YAO ,  Bo LIU ,  Ming LU ,  Feng CHEN ,  Yurong CHEN

IPC: G06V10/82

CPC classification number: G06V10/82

Abstract: Omni-scale convolution for convolutional neural networks is disclosed. An example of an apparatus includes one or more processors to process data, including processing for a convolutional neural network (CNN); and a memory to store data, including CNN data, wherein processing of input data by the CNN includes implementing omni-scale convolution in one or more convolutional layers of the CNN, implementation of the omni-scale convolution into a convolutional layer of the one or more convolutional layers including at least applying multiple dilation rates in a plurality of kernels of a kernel lattice of the convolutional layer, and applying a cyclic pattern for the multiple dilation rates in the plurality of kernels of the convolutional layer.

公开(公告)号：US20200082198A1

公开(公告)日：2020-03-12

申请号：US16609732

申请日：2018-05-22

Applicant: INTEL CORPORATION

Inventor： Anbang YAO ,  Hao ZHAO ,  Ming LU ,  Yiwen GUO ,  Yurong CHEN

IPC: G06K9/46 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: Methods and apparatus for discriminative semantic transfer and physics-inspired optimization in deep learning are disclosed. A computation training method for a convolutional neural network (CNN) includes receiving a sequence of training images in the CNN of a first stage to describe objects of a cluttered scene as a semantic segmentation mask. The semantic segmentation mask is received in a semantic segmentation network of a second stage to produce semantic features. Using weights from the first stage as feature extractors and weights from the second stage as classifiers, edges of the cluttered scene are identified using the semantic features.