US 12,347,157 B2
Selection of video frames using a machine learning predictor
Aneesh Vartakavi, Emeryville, CA (US); and Casper Lützhøft Christensen, Emeryville, CA (US)
Assigned to Gracenote, Inc., New York, NY (US)
Filed by Gracenote, Inc., New York, NY (US)
Filed on Aug. 16, 2023, as Appl. No. 18/234,682.
Application 18/234,682 is a continuation of application No. 17/462,208, filed on Aug. 31, 2021, granted, now 11,776,234.
Application 17/462,208 is a continuation of application No. 16/749,724, filed on Jan. 22, 2020, granted, now 11,145,065, issued on Oct. 12, 2021.
Prior Publication US 2024/0071027 A1, Feb. 29, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 10/00 (2022.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06T 7/174 (2017.01); G06V 10/25 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 20/40 (2022.01)
CPC G06V 10/25 (2022.01) [G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06T 7/174 (2017.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 20/46 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
inputting to a machine learning (ML) predictor program implemented on a computing device a first plurality of training raw images, each respective training raw image of the first plurality being associated with a respective set of training master images, each training master image of a given respective set of training master images indicating respective pre-defined cropping characteristics for the associated respective training raw image, wherein the ML predictor program is configured to generate predicted cropping characteristics for any given input image, wherein cropping characteristics for any particular input image comprise coordinates of cropping boundaries with respect to the particular input image prior to cropping, and wherein the pre-defined cropping characteristics of each respective set of training master images define one or more rectangular training bounding boxes, each enclosing a respective region of interest (ROI) of the associated training raw image;
training the ML predictor program to predict cropping characteristics for each respective training raw image based on the pre-defined cropping characteristics represented in the associated respective set of training master images;
subsequent to training the ML predictor program with the first plurality of training raw images, applying the trained ML predictor program to a second plurality of runtime raw still images in order to determine for each respective runtime raw still image of the second plurality a respective set of runtime cropping characteristics; and
storing, in non-transitory computer-readable memory, the second plurality of runtime raw still images together with information indicative of the respective set of runtime cropping characteristics for each respective runtime raw still image of the second plurality.