US 11,941,085 B2
Extracting video clips from videos for use in training a machine-learning model
Daili Zhang, Houston, TX (US); Varun Tyagi, Houston, TX (US); Fahad Ahmad, Houston, TX (US); Olivier Roger Germain, Houston, TX (US); and Julien Christian Marck, Houston, TX (US)
Assigned to Halliburton Energy Services, Inc., Houston, TX (US)
Filed by Halliburton Energy Services, Inc., Houston, TX (US)
Filed on Nov. 11, 2021, as Appl. No. 17/524,100.
Prior Publication US 2023/0141250 A1, May 11, 2023
Int. Cl. G06T 7/20 (2017.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06V 20/40 (2022.01)
CPC G06F 18/2155 (2023.01) [G06N 20/00 (2019.01); G06T 7/20 (2013.01); G06V 20/46 (2022.01); G06V 20/49 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors; and
one or more memories including program code that is executable by the one or more processors for causing the one or more processors to:
train a model with a set of training data to identify an object and a corresponding spatial location of the object in each image frame in a video depicting performance of a wellsite activity;
analyze a plurality of consecutive image frames in the video using the trained model to identify a target image frame in which the object is present in a predefined spatial area thereof, wherein the predefined spatial area is associated with performance of the wellsite activity; and
generate a video clip that includes only a subpart of the video based on the target image frame, the subpart including a series of consecutive image frames containing the target image frame.