Learning method and learning device of pedestrian detector for robust surveillance based on image analysis by using gan and testing method and testing device using the same

Abstract

A method for learning a pedestrian detector to be used for robust surveillance or military purposes based on image analysis is provided for a solution to a lack of labeled images and for a reduction of annotation costs. The method can be also performed by using generative adversarial networks (GANs). The method includes steps of: a learning device generating an image patch by cropping each of regions on a training image, and instructing an adversarial style transformer to generate a transformed image patch by converting each of pedestrians into transformed pedestrians capable of impeding a detection; and generating a transformed training image by replacing each of the regions with the transformed image patch, instructing the pedestrian detector to detecting the transformed pedestrians, and learning parameters of the pedestrian detector to minimize losses. This learning, as a self-evolving system, is robust to adversarial patterns by generating training data including hard examples.