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1.发明授权公开(公告)号：US11315262B1
公开(公告)日：2022-04-26
申请号：US16909824
申请日：2020-06-23
Applicant: Amazon Technologies, Inc.
Inventor： Boris Cherevatsky , Roman Goldenberg , Gerard Guy Medioni , Ofer Meidan , Ehud Benyamin Rivlin , Dilip Kumar
Abstract: The motion of objects within a scene may be detected and tracked using digital (e.g., visual and depth) cameras aligned with fields of view that overlap at least in part. Objects may be identified within visual images captured from the scene using a tracking algorithm and correlated to point clouds or other depth models generated based on depth images captured from the scene. Once visual aspects (e.g., colors or other features) of objects are correlated to the point clouds, shapes and/or positions of the objects may be determined and used to further train the tracking algorithms to recognize the objects in subsequently captured frames. Moreover, a Kalman filter or other motion modeling technique may be used to enhance the prediction of a location of an object within subsequently captured frames.
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公开(公告)号：US11526693B1
公开(公告)日：2022-12-13
申请号：US16865167
申请日：2020-05-01
Applicant: Amazon Technologies, Inc.
Inventor： Roman Goldenberg , Miriam Farber , George Leifman , Gerard Guy Medioni
Abstract: Disclosed are systems and method for training an ensemble of machine learning models with a focus on feature engineering. For example, the training of the models encourages each machine learning model of the ensemble to rely on a different set of input features from the training data samples used to train the machine learning models of the ensemble. However, instead of telling each model explicitly which features to learn, in accordance with the disclosed implementations, ML models of the ensemble may be trained sequentially, with each new model trained to disregard input features learned by previously trained ML models of the ensemble and learn based on other features included in the training data samples.
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3.发明授权公开(公告)号：US10699421B1
公开(公告)日：2020-06-30
申请号：US15473430
申请日：2017-03-29
Applicant: Amazon Technologies, Inc.
Inventor： Boris Cherevatsky , Roman Goldenberg , Gerard Guy Medioni , Ofer Meidan , Ehud Benyamin Rivlin , Dilip Kumar
Abstract: The motion of objects within a scene may be detected and tracked using digital (e.g., visual and depth) cameras aligned with fields of view that overlap at least in part. Objects may be identified within visual images captured from the scene using a tracking algorithm and correlated to point clouds or other depth models generated based on depth images captured from the scene. Once visual aspects (e.g., colors or other features) of objects are correlated to the point clouds, shapes and/or positions of the objects may be determined and used to further train the tracking algorithms to recognize the objects in subsequently captured frames. Moreover, a Kalman filter or other motion modeling technique may be used to enhance the prediction of a location of an object within subsequently captured frames.
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公开(公告)号：US10534965B2
公开(公告)日：2020-01-14
申请号：US15926745
申请日：2018-03-20
Applicant: Amazon Technologies, Inc.
Inventor： Nitin Singhal , Vivek Bhadauria , Ranju Das , Gaurav D. Ghare , Roman Goldenberg , Stephen Gould , Kuang Han , Jonathan Andrew Hedley , Gowtham Jeyabalan , Vasant Manohar , Andrea Olgiati , Stefano Stefani , Joseph Patrick Tighe , Praveen Kumar Udayakumar , Renjun Zheng
Abstract: Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.
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Patent Agency Ranking
公开(公告)号：US12073571B1
公开(公告)日：2024-08-27
申请号：US17727452
申请日：2022-04-22
Applicant: Amazon Technologies, Inc.
Inventor： Boris Cherevatsky , Roman Goldenberg , Gerard Guy Medioni , Ofer Meidan , Ehud Benyamin Rivlin , Dilip Kumar
IPC: G06T7/292 , G06F18/2113 , G06F18/2415 , G06T7/55 , G06T11/60 , H04N7/18 , H04N23/90
CPC classification number: G06T7/292 , G06F18/2113 , G06F18/2415 , G06T7/55 , G06T11/60 , H04N7/181 , H04N7/188 , H04N23/90 , G06T2207/10024 , G06T2207/10028 , G06T2207/20081 , G06T2210/12
Abstract: The motion of objects within a scene may be detected and tracked using digital (e.g., visual and depth) cameras aligned with fields of view that overlap at least in part. Objects may be identified within visual images captured from the scene using a tracking algorithm and correlated to point clouds or other depth models generated based on depth images captured from the scene. Once visual aspects (e.g., colors or other features) of objects are correlated to the point clouds, shapes and/or positions of the objects may be determined and used to further train the tracking algorithms to recognize the objects in subsequently captured frames. Moreover, a Kalman filter or other motion modeling technique may be used to enhance the prediction of a location of an object within subsequently captured frames.
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公开(公告)号：US12230052B1
公开(公告)日：2025-02-18
申请号：US16712655
申请日：2019-12-12
Applicant: AMAZON TECHNOLOGIES, INC.
Inventor： Igor Kviatkovsky , Nadav Israel Bhonker , Yevgeni Nogin , Roman Goldenberg , Manoj Aggarwal , Gerard Guy Medioni
IPC: G06K9/00 , G06F18/2131 , G06F18/214 , G06K9/62 , G06T3/00 , G06T3/04 , G06T7/70 , G06V40/12
Abstract: Images of a hand are obtained by a camera. A pose of the hand relative to the camera may vary due to rotation, translation, articulation of joints in the hand, and so forth. Avatars comprising texture maps from images of actual hands and three-dimensional models that describe the shape of those hands are manipulated into different poses and articulations to produce synthetic images. Given that the mapping of points on an avatar to the synthetic image is known, highly accurate annotation data is produced that relates particular points on the avatar to the synthetic image. An artificial neural network (ANN) is trained using the synthetic images and corresponding annotation data. The trained ANN processes a first image of a hand to produce a second image of the hand that appears to be in a standardized or canonical pose. The second image may then be processed to identify the user.
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公开(公告)号：US11636286B1
公开(公告)日：2023-04-25
申请号：US16865187
申请日：2020-05-01
Applicant: Amazon Technologies, Inc.
Inventor： Roman Goldenberg , Miriam Farber , George Leifman , Gerard Guy Medioni
Abstract: Described are systems and methods for training machine learning models of an ensemble of models that are de-correlated. For example, two or more machine learning models may be concurrently trained (e.g., co-trained) while adding a decorrelation component to one or both models that decreases the pairwise correlation between the outputs of the models. Unlike traditional approaches, in accordance with the disclosed implementations, only the negative results need to be decorrelated.
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公开(公告)号：US10733450B1
公开(公告)日：2020-08-04
申请号：US16291632
申请日：2019-03-04
Applicant: Amazon Technologies, Inc.
Inventor： Roman Goldenberg , Gerard Guy Medioni , Ofer Meidan , Ehud Benyamin Rivlin , Dilip Kumar
Abstract: Multiple video files that are captured by calibrated imaging devices may be annotated based on a single annotation of an image frame of one of the video files. An operator may enter an annotation to an image frame via a user interface, and the annotation may be replicated from the image frame to other image frames that were captured at the same time and are included in other video files. Annotations may be updated by the operator and/or tracked in subsequent image frames. Predicted locations of the annotations in subsequent image frames within each of the video files may be determined, e.g., by a tracker, and a confidence level associated with any of the annotations may be calculated. Where the confidence level falls below a predetermined threshold, the operator may be prompted to delete or update the annotation, or the annotation may be deleted.
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公开(公告)号：US10223591B1
公开(公告)日：2019-03-05
申请号：US15474946
申请日：2017-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Roman Goldenberg , Gerard Guy Medioni , Ofer Meidan , Ehud Benyamin Rivlin , Dilip Kumar
Abstract: Multiple video files that are captured by calibrated imaging devices may be annotated based on a single annotation of an image frame of one of the video files. An operator may enter an annotation to an image frame via a user interface, and the annotation may be replicated from the image frame to other image frames that were captured at the same time and are included in other video files. Annotations may be updated by the operator and/or tracked in subsequent image frames. Predicted locations of the annotations in subsequent image frames within each of the video files may be determined, e.g., by a tracker, and a confidence level associated with any of the annotations may be calculated. Where the confidence level falls below a predetermined threshold, the operator may be prompted to delete or update the annotation, or the annotation may be deleted.
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