A learning method and a learning device for improving segmentation performance used for detecting a road user event by utilizing a double embedding configuration in a multi-camera system, and a testing method and a testing device using the learning method and a learning device. {LEARNING METHOD AND LEARNING DEVICE FOR IMPROVING SEGMENTATION PERFORMANCE TO BE USED FOR DETECTING ROAD USER EVENTS USING DOUBLE EMBEDDING CONFIGURATION IN MULTI-CAMERA SYSTEM AND TESTING METHOD AND TESTING DEVICE USING THE SAME}

摘要

A learning method for improving segmentation performance to be used for detecting road user events including pedestrian events and vehicle events using double embedding configuration in a multi-camera system is provided. The learning method includes steps of: a learning device instructing similarity convolutional layer to generate similarity embedding feature by applying similarity convolution operations to a feature outputted from a neural network; instructing similarity loss layer to output a similarity loss by referring to a similarity between two points sampled from the similarity embedding feature, and its corresponding GT label image; instructing distance convolutional layer to generate distance embedding feature by applying distance convolution operations to the similarity embedding feature; instructing distance loss layer to output a distance loss for increasing inter-class differences among mean values of instance classes and decreasing intra-class variance values of the instance classes; backpropagating at least one of the similarity loss and the distance loss.