Method and electronic device for training neural network model by augmenting image representing object captured by multiple cameras

An electronic device communicated with an external device including a first camera, comprising:

• a second camera;
• a communication unit;
• a memory configured to store at least one instruction;
• at least one processor operatively connected to the second camera, the communication unit, and the memory and configured to execute the at least one instruction to;
• obtain, through the communication unit, a first object recognition result predicted by a first neural network model using, as an input, a first image captured from a first viewpoint by the first camera of the external device facing a space including at least one object,
• capture a second image by the 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 of the external device 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 that corresponds 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.

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.