SYSTEMS AND METHODS FOR RECOGNIZING NON-LINE-OF-SIGHT HUMAN ACTIONS

    公开(公告)号:US20240428620A1

    公开(公告)日:2024-12-26

    申请号:US18823150

    申请日:2024-09-03

    Abstract: Provided are a system and a method for recognizing non-line-of-sight human action, the method including receiving a plurality of image frames in a sequential order from an imaging device, wherein at least one of the plurality of image frames comprises at least one entity performing an action; identifying, based on the plurality of image frames, that a first partial portion of the action occurs within a field of view of the imaging device and a second partial portion of the action occurs outside the field of view of the imaging device; identifying a type of a motion which occurs during the action based on the first partial portion; extrapolating the motion based on the first partial portion, and generating a trajectory of the motion corresponding to the second partial portion of the action; and recognizing the human action from the type of the motion and the trajectory of the motion.

    METHOD AND ELECTRONIC DEVICE FOR ESTIMATING A LANDMARK POINT OF BODY PART OF SUBJECT

    公开(公告)号:US20240312176A1

    公开(公告)日:2024-09-19

    申请号:US18671412

    申请日:2024-05-22

    CPC classification number: G06V10/273 G06V10/82 G06V40/11

    Abstract: A method performed by an electronic device for estimating a landmark point of a body part of subject by electronic device is provided. The method includes generating, by the electronic device, an initial coarse estimation of the landmark point of the body part using a light-weight deep neural network, determining, by the electronic device, an occluded region of the body part based on the generated initial coarse estimation of the landmark point using a segmentation mask, estimating, by the electronic device, the occlusion probability for the landmark point in the at least one occluded region and the generated initial coarse estimation, determining, by the electronic device, a correction factor for applying on the generated initial coarse estimation as a measure of the estimated occlusion probability, and selecting, by the electronic device, a pre-defined number of neural networks by applying the determined correction factor for processing the at least one occluded region and the generated initial coarse estimation to generate final estimation of the landmark point.

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