公开(公告)号：US20240296528A1

公开(公告)日：2024-09-05

申请号：US18366604

申请日：2023-08-07

申请人： China Jiliang University ,  ZHEJIANG UNIVERSITY OF TECHNOLOGY

发明人： Gang Xiao ,  Jiawei Lu ,  Qibing Wang ,  Chen Li ,  Hao HU ,  Junbo Yao

IPC分类号： G06T5/00

CPC分类号： G06T5/70 ,  G06T2207/10028

摘要： A denoising method based on a multiscale distribution score for a point cloud includes: constructing a two-layer network model based on multiscale perturbation and point cloud distribution, where the two-layer network model includes a feature extraction module for extracting a feature of the point cloud and a displacement prediction module for predicting a displacement of a noise point; constructing a point cloud noise model for improving a denoising effect and retaining a sharp feature and avoiding reducing quality of point cloud data; extracting a global feature h by inputting the point cloud data into the feature extraction module; iteratively learning the displacement of the noise point by the displacement prediction module according to a feature obtained by the feature extraction unit; and defining a loss function of network training, and completing convergence under the condition that the loss function reaches a set threshold or a maximum number of iterations.

公开(公告)号：US20240176933A1

公开(公告)日：2024-05-30

申请号：US18366588

申请日：2023-08-07

申请人： China Jiliang University

发明人： Gang Xiao ,  Jiawei Lu ,  Qibing Wang ,  Chen Li ,  Nan LV ,  Jinjin Dong ,  Hairui Gu

IPC分类号： G06F30/27 ,  G06T17/00 ,  G06T19/20 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06V20/52 ,  G06V20/64 ,  G06V40/20

CPC分类号： G06F30/27 ,  G06T17/00 ,  G06T19/20 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G06V20/52 ,  G06V20/64 ,  G06V40/23 ,  G06T2219/2004

摘要： A digital twin based method for monitoring behavior of a passenger on an escalator includes: first, constructing a digital twin virtual scene of the escalator carrying the passenger; constructing a virtual person model in a physics engine, generating various types of risky behavior in reality, and outputting different types of behavior of a virtual person; second, through a human posture recognition method for monitoring behavior of a passenger, extracting a feature map as an input by means of a visual geometry group 19 (VGG19) pre-training network, to enter part affinity fields (PAFs) and a part confidence map (PCM), so as to complete recognition of human postures; classifying the human postures with a support vector machine; and performing emergency stop control on the escalator under risky behavior of the passenger according to a result of posture classification.