Selection of video frames using a machine learning predictor

A system comprising:

• one or more processors;
• and
• memory storing instructions that, when executed by the one or more processors, cause the system to carry out operations of a machine learning (ML) predictor program, wherein the operations include:
• receiving as input to the ML predictor program 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 sequence of video image frames in order to determine for each respective video image frame of the sequence a respective score corresponding to a highest statistical confidence from among confidence levels respectively associated with one or more subsets of cropping characteristics predicted for the respective video image frame by the trained ML predictor program;
• and
• selecting and storing, in non-transitory computer-readable memory, information indicative of the respective video image frame having the highest respective score.

The system of 14, wherein selecting and storing, in non-transitory computer-readable memory, the information indicative of the respective video image frame having the highest respective score comprises:

• determining the highest score from among the respective scores;
• identifying the respective video frame associated with the highest score;
• and
• storing the identified respective video frame together with information identifying both the respective video frame and the subset associated with respective confidence level corresponding to the highest score.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more processors of a system, cause the system to carry out operations of a machine learning (ML) predictor program, wherein the operations include:

• receiving as input to the ML predictor program 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 sequence of video image frames in order to determine for each respective video image frame of the sequence a respective score corresponding to a highest statistical confidence from among confidence levels respectively associated with one or more subsets of cropping characteristics predicted for the respective video image frame by the trained ML predictor program;
• and
• selecting and storing, in non-transitory computer-readable memory, information indicative of the respective video image frame having the highest respective score.