Host: The Japanese Society for Artificial Intelligence
Name : 34th Annual Conference, 2020
Number : 34
Location : Online
Date : June 09, 2020 - June 12, 2020
In this paper, we propose a method of segmenting and learning continuous motions depending on reference points by extending the model proposed by Taniguchi et al. As input, time series data of a continuous motion based on relative coordinates from each candidate are given, and reference point estimation and segmentation of the continuous motion are performed simultaneously. In experiments, the proposed method can segment continuous motions into individual motions more correctly than the conventional method. This indicates that estimation of reference objects improves the segmentation accuracy.