Object scanning using planar segmentation

    公开(公告)号:US11361508B2

    公开(公告)日:2022-06-14

    申请号:US16998975

    申请日:2020-08-20

    Abstract: Techniques are provided for generating three-dimensional models of objects from one or more images or frames. For example, at least one frame of an object in a scene can be obtained. A portion of the object is positioned on a plane in the at least one frame. The plane can be detected in the at least one frame and, based on the detected plane, the object can be segmented from the plane in the at least one frame. A three-dimensional (3D) model of the object can be generated based on segmenting the object from the plane. A refined mesh can be generated for a portion of the 3D model corresponding to the portion of the object positioned on the plane.

    Methods and systems for applying complex object detection in a video analytics system

    公开(公告)号:US11004209B2

    公开(公告)日:2021-05-11

    申请号:US16158079

    申请日:2018-10-11

    Abstract: Techniques and systems are provided for tracking objects in one or more video frames. For example, a first set of one or more bounding regions are determined for a video frame based on a trained classification network applied to the video frame. The first set of one or more bounding regions are associated with one or more objects in the video frame. One or more blobs can be detected for the video frame. A blob includes pixels of at least a portion of an object in the video frame. A second set of one or more bounding regions are determined for the video frame that are associated with the one or more blobs. A final set of one or more bounding regions is determined for the video frame using the first set of one or more bounding regions and the second set of one or more bounding regions. Object tracking can then be performed for the video frame using the final set of one or more bounding regions.

    Depth image based face anti-spoofing

    公开(公告)号:US10956719B2

    公开(公告)日:2021-03-23

    申请号:US16206832

    申请日:2018-11-30

    Abstract: Methods, systems, and devices for image processing are described. The method may include identifying a face in a first image based on identifying one or more biometric features of the face, determining an angular direction of one or more pixels of the identified face, identifying an anchor point on the identified face, sorting each of one or more pixels of the identified face into one of a set of pixel bins based on a combination of the determined angular direction of the pixel and a distance between the pixel and the identified anchor point, and outputting an indication of authenticity associated with the face based on a number of pixels in each bin.

    DEPTH IMAGE BASED FACE ANTI-SPOOFING
    55.
    发明申请

    公开(公告)号:US20200175260A1

    公开(公告)日:2020-06-04

    申请号:US16206832

    申请日:2018-11-30

    Abstract: Methods, systems, and devices for image processing are described. The method may include identifying a face in a first image based on identifying one or more biometric features of the face, determining an angular direction of one or more pixels of the identified face, identifying an anchor point on the identified face, sorting each of one or more pixels of the identified face into one of a set of pixel bins based on a combination of the determined angular direction of the pixel and a distance between the pixel and the identified anchor point, and outputting an indication of authenticity associated with the face based on a number of pixels in each bin.

    Methods and systems for performing sleeping object detection and tracking in video analytics

    公开(公告)号:US10282617B2

    公开(公告)日:2019-05-07

    申请号:US15645455

    申请日:2017-07-10

    Abstract: Methods, apparatuses, and computer-readable media are provided for maintaining blob trackers for video frames. For example, a first blob tracker maintained for a current video frame is identified. The first blob tracker is associated with a blob detected in one or more video frames. The blob includes pixels of at least a portion of a foreground object in the one or more video frames. It is determined that the first blob tracker is a first type of tracker. Trackers having the first type are associated with objects that have transitioned at least partially into a background model (referred to as sleeping objects and sleeping trackers). One or more interactions are identified between the first blob tracker and at least one other blob tracker. The at least one other blob tracker can be the first type of tracker or can be a second type of tracker that is not a sleeping tracker (the second type of tracker is not associated with an object that has transitioned at least partially into the background model. A characteristic of the first blob tracker can then be modified based on the identified one or more interactions. Modifying the characteristic of the first blob tracker can include transitioning the first blob tracker from the first type of tracker to the second type of tracker, updating an appearance model of the first blob tracker, and/or other suitable characteristic of the first blob tracker.

    Foveated video rendering
    58.
    发明授权

    公开(公告)号:US10157448B2

    公开(公告)日:2018-12-18

    申请号:US15270983

    申请日:2016-09-20

    Abstract: Techniques are described for generating and rendering video content based on area of interest (also referred to as foveated rendering) to allow 360 video or virtual reality to be rendered with relatively high pixel resolution even on hardware not specifically designed to render at such high pixel resolution. Processing circuitry may be configured to keep the pixel resolution within a first portion of an image of one view at the relatively high pixel resolution, but reduce the pixel resolution through the remaining portions of the image of the view based on an eccentricity map and/or user eye placement. A device may receive the images of these views and process the images to generate viewable content (e.g., perform stereoscopic rendering or interpolation between views). Processing circuitry may also make use of future frames within a video stream and base predictions on those future frames.

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