TY - GEN
T1 - Robustly Predicting Pedestrian Destinations Using Pre-trained Machine Learning Model for a Voice Guidance Robot∗
AU - Ohta, Asami
AU - Okano, Satoshi
AU - Matsuhira, Nobuto
AU - Kato, Yuka
N1 - Funding Information:
This work was partially supported by JSPS KAKENHI (17K00366, 17KT0080) and the Cooperative Research Project Program of the Research Institute of Electrical Communication, Tohoku University.
PY - 2019/10
Y1 - 2019/10
N2 - In this paper, we propose a method robustly predicting the destination of a pedestrian heading toward a robot in order to provide suitable voice guidance to him/her by communication robots installed at the reception desks of public facilities. For this purpose, we measure a pedestrian trajectory with a laser range scanner attached to the robot, and predict the destination among more than three branches by cascading multiple predictor models for two branches pre-trained by a machine learning algorithm. In order to verify the effectiveness of the proposed method, we conduct experiments using a dataset of tracking pedestrians at a shopping mall, and data observed in the real environment. The result shows that our method can predict three branch destinations with an accuracy of about 80%.
AB - In this paper, we propose a method robustly predicting the destination of a pedestrian heading toward a robot in order to provide suitable voice guidance to him/her by communication robots installed at the reception desks of public facilities. For this purpose, we measure a pedestrian trajectory with a laser range scanner attached to the robot, and predict the destination among more than three branches by cascading multiple predictor models for two branches pre-trained by a machine learning algorithm. In order to verify the effectiveness of the proposed method, we conduct experiments using a dataset of tracking pedestrians at a shopping mall, and data observed in the real environment. The result shows that our method can predict three branch destinations with an accuracy of about 80%.
KW - dataset
KW - machine learning
KW - pedestrian model
KW - pre-trained predictor
KW - service robot
UR - http://www.scopus.com/inward/record.url?scp=85084140509&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084140509&partnerID=8YFLogxK
U2 - 10.1109/IECON.2019.8927554
DO - 10.1109/IECON.2019.8927554
M3 - Conference contribution
AN - SCOPUS:85084140509
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 6922
EP - 6927
BT - Proceedings
PB - IEEE Computer Society
T2 - 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Y2 - 14 October 2019 through 17 October 2019
ER -