Proposal of a Method for Reducing the Man-hours Required to Create Teacher Data for In-Vehicle Camera Videos

Abstract

Today, many devices such as smartphones and cars are equipped with cameras thanks to the development of various types of hardware and software. In particular, in-vehicle cameras are expected to be applied not only to vehicle operations such as automated driving technology, which has been rapidly developing in recent years, but also to recognition of the outside world, such as detection of changes in objects on the road. Generally, deep learning methods are used for such processing involving recognition from video images, but this requires a large amount of teacher data, which is currently selected manually. On the other hand, manually assigning teacher labels to video data takes several times longer than the actual video time. Therefore, the cost of labeling is not only a problem, but also the mislabeling caused by human identification errors. Therefore, we propose an efficient annotation workflow to reduce the man-hours required to create teacher data for in-vehicle camera videos. Based on a model that captures the characteristics of in-vehicle camera videos, we show that this workflow can be used to perform teacher labeling (annotation) with a minimum number of man-hours. By capturing the characteristics of the in-vehicle camera video and reflecting them in the identification model in the workflow, the proposed workflow resulted in a reduction of about 77.5% of man-hours.