TY - GEN
T1 - Normal Vector Direction-based 3D LiDAR Point Cloud Planar Surface Removal for Object Cluster Minimization in Human Activity Monitoring System
AU - Budiyanta, Nova Eka
AU - Yuniarno, Eko Mulyanto
AU - Usagawa, Tsuyoshi
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The use of 3-Dimensional Light Detection and Ranging (3D LiDAR) point cloud as the alternative data to reduce privacy exposure in monitoring systems has been carried out in several studies. Unfortunately, various challenges in using point clouds intersect with the amount of data and computational costs. Several studies attempted to optimize the point cloud processing approach by segmenting the ground plane to get the object clusters separated. However, many unnecessary points can still burden the computation process. Since the ground plane mainly represents the horizontal planar plane on the x, y axis, this study tried to reduce the points on the vertical planar plane on the x, z and y, z axes with the x, y horizontal planar plane as well based on the surface normal vector direction of each point. The proposed approach has successfully reduced the raw point cloud by 79.29% removing the point cloud that indicates the planar surface of the three axes while maintaining the essential object of the monitoring system on the KITTI raw dataset. Therefore, the object cluster can be minimized, supporting the computational costs for further research in human activity monitoring systems.
AB - The use of 3-Dimensional Light Detection and Ranging (3D LiDAR) point cloud as the alternative data to reduce privacy exposure in monitoring systems has been carried out in several studies. Unfortunately, various challenges in using point clouds intersect with the amount of data and computational costs. Several studies attempted to optimize the point cloud processing approach by segmenting the ground plane to get the object clusters separated. However, many unnecessary points can still burden the computation process. Since the ground plane mainly represents the horizontal planar plane on the x, y axis, this study tried to reduce the points on the vertical planar plane on the x, z and y, z axes with the x, y horizontal planar plane as well based on the surface normal vector direction of each point. The proposed approach has successfully reduced the raw point cloud by 79.29% removing the point cloud that indicates the planar surface of the three axes while maintaining the essential object of the monitoring system on the KITTI raw dataset. Therefore, the object cluster can be minimized, supporting the computational costs for further research in human activity monitoring systems.
KW - 3D LiDAR
KW - Normal Vector Direction
KW - Object Cluster Minimization
KW - Planar Surface Re-moval
KW - Point Cloud
UR - http://www.scopus.com/inward/record.url?scp=85166367693&partnerID=8YFLogxK
U2 - 10.1109/I2MTC53148.2023.10175928
DO - 10.1109/I2MTC53148.2023.10175928
M3 - Conference contribution
AN - SCOPUS:85166367693
T3 - Conference Record - IEEE Instrumentation and Measurement Technology Conference
BT - I2MTC 2023 - 2023 IEEE International Instrumentation and Measurement Technology Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2023
Y2 - 22 May 2023 through 25 May 2023
ER -