US 12,347,119 B2
Training a machine learning algorithm to perform motion estimation of objects in a set of frames
Aria Ahmadi, Hertfordshire (GB); David Walton, Hertfordshire (GB); and Cagatay Dikici, Hertfordshire (GB)
Assigned to Imagination Technologies Limited, Kings Langley (GB)
Filed by Imagination Technologies Limited, Kings Langley (GB)
Filed on Mar. 28, 2024, as Appl. No. 18/619,844.
Application 18/619,844 is a division of application No. 17/187,831, filed on Feb. 28, 2021, granted, now 12,073,567.
Claims priority of application No. 2002767 (GB), filed on Feb. 27, 2020; and application No. 2100666 (GB), filed on Jan. 19, 2021.
Prior Publication US 2024/0265556 A1, Aug. 8, 2024
Int. Cl. G06T 7/246 (2017.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06T 7/207 (2017.01)
CPC G06T 7/246 (2017.01) [G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06T 7/207 (2017.01); G06T 7/248 (2017.01); G06T 2207/20081 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of generating a training dataset for training a machine learning algorithm to perform motion estimation, the method comprising:
obtaining a plurality of images of objects;
obtaining a plurality of images of backgrounds; and
generating a plurality of pairs of synthetic images, each pair comprising a first frame and a second frame; wherein
the first frame comprises a selection of objects in first positions and first orientations superimposed on a selected background,
the second frame comprises the selection of objects in second positions and second orientations superimposed on the selected background, and
at least some of the second positions and second orientations are different from the first positions and first orientations;
the method further comprising
generating translational ground truth motion vectors, describing the difference between the first positions and the second positions; and
generating non-translational ground truth motion vectors, describing the difference between the first orientations and the second orientations.