US 12,462,536 B2
Method and electronic device for training neural network model by augmenting image representing object captured by multiple cameras
Jaeyong Ju, Suwon-si (KR)
Assigned to SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed by SAMSUNG ELECTRONICS CO., LTD., Suwon-si (KR)
Filed on Oct. 27, 2023, as Appl. No. 18/384,549.
Application 18/384,549 is a continuation of application No. PCT/KR2023/016525, filed on Oct. 24, 2023.
Claims priority of application No. 10-2022-0137781 (KR), filed on Oct. 24, 2022; and application No. 10-2022-0169100 (KR), filed on Dec. 6, 2022.
Prior Publication US 2024/0135686 A1, Apr. 25, 2024
Prior Publication US 2024/0233340 A9, Jul. 11, 2024
Int. Cl. G06V 10/82 (2022.01); G06T 7/80 (2017.01); G06V 10/774 (2022.01); G06V 20/70 (2022.01); H04N 23/90 (2023.01)
CPC G06V 10/774 (2022.01) [G06T 7/80 (2017.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01); H04N 23/90 (2023.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30204 (2013.01); G06T 2207/30244 (2013.01)] 20 Claims
OG exemplary drawing
 
19. A cloud server comprising:
a communication unit;
a memory storing at least one instruction;
at least one processor operatively connected to the communication unit and the memory and configured to execute the at least one instruction to:
obtain a first object recognition result predicted by a first neural network model using, as an input, a first image captured by a first camera facing a space including at least one object from a first viewpoint,
obtain, through the communication unit, a second image captured by a second camera facing the space from a second viewpoint,
obtain a label for the second image by converting the obtained first object recognition result, based on a conversion relationship between a first camera coordinate system corresponding to the first camera and a second camera coordinate system corresponding to the second camera,
generate training data by performing labeling, using the obtained label for the second image, on the second image corresponding to the first image, based on the first object recognition result converted with respect to the second viewpoint, and
train a second neural network model by using the generated training data.