TY - GEN
T1 - Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network
AU - Riansyah, Moch Iskandar
AU - Sardjono, Tri Arief
AU - Yuniarno, Eko Mulyanto
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Robot interaction with humans requires intelligent robots that can understand human activities. The development of advanced 3D LiDAR sensors has greatly contributed to this capability. In this study, we specifically focus on the use of 3D LiDAR sensors to predict the orientation of the human body using 3D Convolutional Neural Networks (CNNs) based on voxelized datasets. The dataset used in this study was created using a 3D LiDAR sensor with 32-channel specifications. We divided the dataset into four categories representing different walking orientations. The goal was to explore the performance of four different 3D CNN architectures using independently generated datasets. Based on the experimental results and performance analysis, it was found that VGG16 outperformed the other three architectures in predicting body orientation. VGG16 achieved an accuracy of 0.95, which was higher than DenseNet121 with approximately 0.91, ResNet50V2 with 0.80, and ResNet50 with 0.73. In the future, this method will be developed with additional orientation and results of architectural testing so that it can be modified to be better for further research on understanding human activity by robots.
AB - Robot interaction with humans requires intelligent robots that can understand human activities. The development of advanced 3D LiDAR sensors has greatly contributed to this capability. In this study, we specifically focus on the use of 3D LiDAR sensors to predict the orientation of the human body using 3D Convolutional Neural Networks (CNNs) based on voxelized datasets. The dataset used in this study was created using a 3D LiDAR sensor with 32-channel specifications. We divided the dataset into four categories representing different walking orientations. The goal was to explore the performance of four different 3D CNN architectures using independently generated datasets. Based on the experimental results and performance analysis, it was found that VGG16 outperformed the other three architectures in predicting body orientation. VGG16 achieved an accuracy of 0.95, which was higher than DenseNet121 with approximately 0.91, ResNet50V2 with 0.80, and ResNet50 with 0.73. In the future, this method will be developed with additional orientation and results of architectural testing so that it can be modified to be better for further research on understanding human activity by robots.
KW - 3D Lidar
KW - CNN
KW - Human body
KW - Orientation
KW - Robot
UR - http://www.scopus.com/inward/record.url?scp=85171130496&partnerID=8YFLogxK
U2 - 10.1109/ISITIA59021.2023.10221066
DO - 10.1109/ISITIA59021.2023.10221066
M3 - Conference contribution
AN - SCOPUS:85171130496
T3 - 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding
SP - 99
EP - 104
BT - 2023 International Seminar on Intelligent Technology and Its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023
Y2 - 26 July 2023 through 27 July 2023
ER -
