Reducing Number of Acquisition Images by Synthesizing Training Samples Using Aspects for Object Detection

Abstract

We propose a novel method for synthesizing training samples to obtain high accuracy of object detection under the condition that the number of acquisition images is small. The convolutional neuronal networks for object detection require the large number of acquisition images that the angles of postures of each object are varied. Thus, it is very time-consuming to collect training samples. On the other hand, our method synthesizes training samples from the small number of aspect images that determine the variation of appearances of objects. We design how to collect aspect images based on the knowledge that there is a bias in the postures of objects. Experimental results show that our method significantly reduces the number of acquisition images while keeping high detection accuracy of a comparison method that requires the large number of acquisition images.