In traffic census, it is expected to develop image processing technologies for counting number of passing automobiles by analyzing video image. Many counting technologies using deep learning have been proposed. It is difficult to maintain sufficient accuracy because new automobiles are sold year after year. Therefore, it is necessary to maintain high accuracy by re-learning training data of automobiles with new shapes and colors continuously. However, maintenance labor cost is huge because training data have to be created continuously. In this research, technique to recursive active learning for segmentation of automobile parts is proposed and clarified its usefulness.
