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Patent Agency Ranking
公开(公告)号：US20210390309A1
公开(公告)日：2021-12-16
申请号：US16898058
申请日：2020-06-10
Applicant: ioNetworks INC.
Inventor： Jing-Ming Guo , Ting Lin , Herleeyandi Markoni , Albert Christianto , Cheng-En Tsai , Hong-Yi Liang , Yi-Hsiang Lin , Shao-Xiong Weng , Chih-Hung Wang , Po-Cheng Huang , Jeffry Susanto
Abstract: A video condensation & recognition method and a system thereof are used to detect objects in input frames, derive a 3D object trajectory from which one or multiple 2D trajectories with object tubes are extracted, and catch a time position that refers to non-overlapping condition of object tubes for rearrangement of several continuous object tubes and creation of a condensed video. The generated condensed video can be adjusted by the user in the range of longest detected object tube to the generated condensed video length that is very beneficial for video analysis process. Moreover, a searched attribute of an object in a condensed video is classified for the filtering process which is able to display an object correlated with a distinct searched attribute on the condensed video.
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公开(公告)号：US11796668B2
公开(公告)日：2023-10-24
申请号：US17218106
申请日：2021-03-30
Applicant: ioNetworks INC.
Inventor： Jing-Ming Guo , Ting Lin , Chia-Fen Chang , Jeffry Susanto , Yi-Hsiang Lin , Po-Cheng Huang , Yu-Wen Wei
CPC classification number: G01S13/867 , G01S7/06 , G06V40/172
Abstract: A radar detection and identification device is disclosed, comprising at least one display host, at least one camera and at least one radar detector, wherein the camera and the radar detector, after photographing and detecting, are capable of performing masked face recognition and radar physiological detection recognition processes in order to identify the identity information and human physiological signals and display them on the display host.
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公开(公告)号：US11328160B2
公开(公告)日：2022-05-10
申请号：US16898058
申请日：2020-06-10
Applicant: ioNetworks INC.
Inventor： Jing-Ming Guo , Ting Lin , Herleeyandi Markoni , Albert Christianto , Cheng-En Tsai , Hong-Yi Liang , Yi-Hsiang Lin , Shao-Xiong Weng , Chih-Hung Wang , Po-Cheng Huang , Jeffry Susanto
Abstract: A video condensation & recognition method and a system thereof are used to detect objects in input frames, derive a 3D object trajectory from which one or multiple 2D trajectories with object tubes are extracted, and catch a time position that refers to non-overlapping condition of object tubes for rearrangement of several continuous object tubes and creation of a condensed video. The generated condensed video can be adjusted by the user in the range of longest detected object tube to the generated condensed video length that is very beneficial for video analysis process. Moreover, a searched attribute of an object in a condensed video is classified for the filtering process which is able to display an object correlated with a distinct searched attribute on the condensed video.
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公开(公告)号：US11992322B2
公开(公告)日：2024-05-28
申请号：US17218142
申请日：2021-03-30
Applicant: ioNetworks INC.
Inventor： Jing-Ming Guo , Ting Lin , Chia-Fen Chang , Jeffry Susanto , Yi-Hsiang Lin , Po-Cheng Huang , Yu-Wen Wei
CPC classification number: A61B5/346 , A61B5/333 , A61B5/7203 , A61B5/7257 , A61B5/7264
Abstract: A heart rhythm detection method and system by using radar sensor is capable of collecting an original signal using a radar sensor toward at least one subject, and converting the original signal to a two dimensional image information (i.e., spectrogram) using the concept of image vision. Then, the neural network automatically learns which heartbeat frequency should be focused on and which heartbeat frequency should be filtered out in the two dimensional image information through deep learning, so that the heartbeat frequencies can be extracted effectively.
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公开(公告)号：US11328535B1
公开(公告)日：2022-05-10
申请号：US17107439
申请日：2020-11-30
Applicant: ioNetworks INC.
Inventor： Jing-Ming Guo , Po-Cheng Huang , Ting Lin , Chih-Hung Wang , Yu-Wen Wei , Yi-Hsiang Lin
Abstract: The present invention provides an action recognition method and system thereof. The action recognition method comprises: capturing a 2D image and a depth image at the same time, extracting an 2D information of the human skeleton points from the 2D image and correcting it, mapping the 2D information of the human skeleton points to the depth image to obtain the corresponding depth information with respect to the 2D information of the human skeleton points and combining the corrected 2D information of the human skeleton points and the depth information to obtain the 3D information of the human skeleton points, and finally recognizing an action from a set of 3D information of the human skeleton points during a period of time by a matching model.
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