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    TARGET ASSOCIATION USING OCCLUSION ANALYSIS, CLUSTERING, OR BOTH
    2.
    发明申请
    TARGET ASSOCIATION USING OCCLUSION ANALYSIS, CLUSTERING, OR BOTH 有权

    公开(公告)号：US20210407107A1

    公开(公告)日：2021-12-30

    申请号：US17348047

    申请日：2021-06-15

    申请人： ObjectVideo Labs, LLC

    发明人： Sung Chun Lee ,  Gang Qian ,  Sima Taheri ,  Sravanthi Bondugula ,  Allison Beach

    IPC分类号： G06T7/246 ,  G06K9/62 ,  G06T7/50 ,  G06T7/90 ,  G06K9/00

    摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for tracking moving objects depicted in multiple images. One of the methods includes determining, for an image captured by a camera, a first bounding box that represents a first moving object depicted in the image, determining that the first bounding box and a second bounding box overlap in an overlap area, determining that the first moving object represented by the first bounding box was farther from the camera that captured the image than a second moving object represented by the second bounding box, generating a mask for the first bounding box based on the overlap area, and determining, using data from the image that is associated with the mask, that the first moving object matches an appearance of another moving object depicted in another image captured by the camera.

    FAST USER ENROLLMENT FOR FACIAL RECOGNITION USING FACE CLUSTERING
    6.
    发明申请
    FAST USER ENROLLMENT FOR FACIAL RECOGNITION USING FACE CLUSTERING 有权

    公开(公告)号：US20230044233A1

    公开(公告)日：2023-02-09

    申请号：US17879219

    申请日：2022-08-02

    申请人： ObjectVideo Labs, LLC

    发明人： Yuanwei Wu ,  Gang Qian ,  Sung Chun Lee ,  Allison Beach

    IPC分类号： G06V40/50 ,  G06V10/762 ,  G06V40/16

    摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for fast user enrollment for facial recognition using face clustering. One of the methods includes identifying, from a set of face images of faces, clusters of face images, where the clusters of face images include a particular cluster; receiving, from a device, an indication that the particular cluster includes a first subcluster of face images that depict a first person and a second subcluster of face images that depict a second person; in response to receiving the indication, determining that a number of face images in the first subcluster of face images that depict the first person does not satisfy an enrollment criteria; identifying another cluster of face images that depict the first person; and enrolling, in a facial recognition database, the first person using the other cluster of face images.

    ITEM MONITORING FOR DOORBELL CAMERAS
    7.
    发明申请
    ITEM MONITORING FOR DOORBELL CAMERAS 有权

    公开(公告)号：US20220083782A1

    公开(公告)日：2022-03-17

    申请号：US17476605

    申请日：2021-09-16

    申请人： ObjectVideo Labs, LLC

    发明人： Gang Qian ,  Allison Beach ,  Sima Taheri ,  Sravanthi Bondugula ,  Sung Chun Lee ,  Narayanan Ramanathan

    IPC分类号： G06K9/00 ,  G06K9/20 ,  G06K9/62 ,  G06K9/32 ,  H04N7/18

    摘要： Methods and systems, including computer programs encoded on a storage medium, are described for implementing item monitoring using a doorbell camera. A system generates an input video stream that has image frames corresponding to detection of activity at a property. Timing information is generated for the video stream and includes a timestamp for each image frame of the stream. Using the timing information, the system processes a pre-event image frame that precedes detection of the activity and a post-event image frame that coincides with detection of the activity. An image score is computed with respect to placement of a candidate item at the property in response to processing the pre-event and post-event image frames. The image score is used to determine that a first item was delivered to the property or that a second item was removed after being delivered to the property.

    Training image classifiers
    8.
    发明授权
    Training image classifiers 有权

    公开(公告)号：US12014271B2

    公开(公告)日：2024-06-18

    申请号：US18108282

    申请日：2023-02-10

    申请人： ObjectVideo Labs, LLC

    发明人： Sravanthi Bondugula ,  Gang Qian ,  Sung Chun Lee ,  Sima Taheri ,  Allison Beach

    IPC分类号： G06N3/08 ,  G06F18/214 ,  G06T7/194 ,  G06V10/20 ,  G06V10/25 ,  G06V10/774 ,  G06V10/82

    CPC分类号： G06N3/08 ,  G06F18/2148 ,  G06T7/194 ,  G06V10/25 ,  G06V10/255 ,  G06V10/7747 ,  G06V10/82

    摘要： Methods, systems, an apparatus, including computer programs encoded on a storage device, for training an image classifier. A method includes receiving an image that includes a depiction of an object; generating a set of poorly localized bounding boxes; and generating a set of accurately localized bounding boxes. The method includes training, at a first learning rate and using the poorly localized bounding boxes, an object classifier to classify the object; and training, at a second learning rate that is lower than the first learning rate, and using the accurately localized bounding boxes, the object classifier to classify the object. The method includes receiving a second image that includes a depiction of an object; and providing, to the trained object classifier, the second image. The method includes receiving an indication that the object classifier classified the object in the second image; and performing one or more actions.

    MOTION-BASED HUMAN VIDEO DETECTION
    10.
    发明申请
    MOTION-BASED HUMAN VIDEO DETECTION 有权

    公开(公告)号：US20230054918A1

    公开(公告)日：2023-02-23

    申请号：US17980715

    申请日：2022-11-04

    申请人： ObjectVideo Labs, LLC

    发明人： Sima Taheri ,  Gang Qian ,  Sung Chun Lee ,  Sravanthi Bondugula ,  Allison Beach

    IPC分类号： G06V40/20 ,  G06T7/254 ,  G06N3/08 ,  G06V10/98

    摘要： Methods, systems, and apparatus for motion-based human video detection are disclosed. A method includes generating a representation of a difference between two frames of a video; providing, to an object detector, a particular frame of the two frames and the representation of the difference between two frames of the video; receiving an indication that the object detector detected an object in the particular frame; determining that detection of the object in the particular frame was a false positive detection; determining an amount of motion energy where the object was detected in the particular frame; and training the object detector based on penalization of the false positive detection in accordance with the amount of motion energy where the object was detected in the particular frame.