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公开(公告)号：US20200349722A1
公开(公告)日：2020-11-05
申请号：US16464608
申请日：2017-12-01
Applicant: Google LLC
Inventor： Cordelia Luise Schmid , Sudheendra Vijayanarasimhan , Susanna Maria Ricco , Bryan Andrew Seybold , Rahul Sukthankar , Aikaterini Fragkiadaki
Abstract: A system comprising an encoder neural network, a scene structure decoder neural network, and a motion decoder neural network. The encoder neural network is configured to: receive a first image and a second image; and process the first image and the second image to generate an encoded representation of the first image and the second image. The scene structure decoder neural network is configured to process the encoded representation to generate a structure output characterizing a structure of a scene depicted in the first image. The motion decoder neural network configured to process the encoded representation to generate a motion output characterizing motion between the first image and the second image.
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公开(公告)号：US11688077B2
公开(公告)日：2023-06-27
申请号：US16954153
申请日：2017-12-15
Applicant: GOOGLE LLC
CPC classification number: G06T7/20 , G06F18/214 , G06N3/045 , G06N3/08 , G06T7/246 , G06T2207/20081 , G06T2207/20084
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a machine-learned object tracking policy. One of the methods includes receiving a current video frame by a user device having a plurality of installed object trackers, wherein each object tracker is configured to perform a different object tracking procedure on the current video frame rent video frame. The current video frame and one or more object tracks previously generated by the one or more object trackers are provided as input to a trained policy engine that implements a reinforcement learning model to generate a particular object tracking plan. A particular object tracking plan is selected based on the output of the reinforcement learning model, and the selected object tracking plan is performed on the current video frame to generate one or more updated object tracks for the current video frame.
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公开(公告)号：US20210166402A1
公开(公告)日：2021-06-03
申请号：US16954153
申请日：2017-12-15
Applicant: GOOGLE LLC
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a machine-learned object tracking policy. One of the methods includes receiving a current video frame by a user device having a plurality of installed object trackers, wherein each object tracker is configured to perform a different object tracking procedure on the current video frame rent video frame. The current video frame and one or more object tracks previously generated by the one or more object trackers are provided as input to a trained policy engine that implements a reinforcement learning model to generate a particular object tracking plan. A particular object tracking plan is selected based on the output of the reinforcement learning model, and the selected object tracking plan is performed on the current video frame to generate one or more updated object tracks for the current video frame.
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公开(公告)号：US11669977B2
公开(公告)日：2023-06-06
申请号：US17214327
申请日：2021-03-26
Applicant: Google LLC
Inventor： Susanna Maria Ricco , Bryan Andrew Seybold
IPC: G06T7/73 , G06T7/215 , G06T7/246 , G06V10/764 , G06V20/40
CPC classification number: G06T7/215 , G06T7/248 , G06T7/74 , G06V10/764 , G06V20/40 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.
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Patent Agency Ranking
公开(公告)号：US10991122B2
公开(公告)日：2021-04-27
申请号：US16264222
申请日：2019-01-31
Applicant: Google LLC
Inventor： Susanna Maria Ricco , Bryan Andrew Seybold
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.
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公开(公告)号：US11763466B2
公开(公告)日：2023-09-19
申请号：US17132623
申请日：2020-12-23
Applicant: Google LLC
Inventor： Cordelia Luise Schmid , Sudheendra Vijayanarasimhan , Susanna Maria Ricco , Bryan Andrew Seybold , Rahul Sukthankar , Aikaterini Fragkiadaki
IPC: G06T7/269 , G06N3/02 , G06T3/40 , G06T9/00 , G06T7/215 , G06T7/70 , G06N3/045 , G06N3/048 , G06V10/82 , G06V10/44
CPC classification number: G06T7/269 , G06N3/045 , G06N3/048 , G06T7/215 , G06T7/70 , G06V10/454 , G06V10/82 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
Abstract: A system comprising an encoder neural network, a scene structure decoder neural network, and a motion decoder neural network. The encoder neural network is configured to: receive a first image and a second image; and process the first image and the second image to generate an encoded representation of the first image and the second image. The scene structure decoder neural network is configured to process the encoded representation to generate a structure output characterizing a structure of a scene depicted in the first image. The motion decoder neural network configured to process the encoded representation to generate a motion output characterizing motion between the first image and the second image.
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公开(公告)号：US20210217197A1
公开(公告)日：2021-07-15
申请号：US17214327
申请日：2021-03-26
Applicant: Google LLC
Inventor： Susanna Maria Ricco , Bryan Andrew Seybold
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.
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公开(公告)号：US20210118153A1
公开(公告)日：2021-04-22
申请号：US17132623
申请日：2020-12-23
Applicant: Google LLC
Inventor： Cordelia Luise Schmid , Sudheendra Vijayanarasimhan , Susanna Maria Ricco , Bryan Andrew Seybold , Rahul Sukthankar , Aikaterini Fragkiadaki
Abstract: A system comprising an encoder neural network, a scene structure decoder neural network, and a motion decoder neural network. The encoder neural network is configured to: receive a first image and a second image; and process the first image and the second image to generate an encoded representation of the first image and the second image. The scene structure decoder neural network is configured to process the encoded representation to generate a structure output characterizing a structure of a scene depicted in the first image. The motion decoder neural network configured to process the encoded representation to generate a motion output characterizing motion between the first image and the second image.
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公开(公告)号：US20200151905A1
公开(公告)日：2020-05-14
申请号：US16264222
申请日：2019-01-31
Applicant: Google LLC
Inventor： Susanna Maria Ricco , Bryan Andrew Seybold
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.
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