Estimation of physical activities of people in offices from time-series point-cloud data

11/17/2022
by   Koki Kizawa, et al.
0

This paper proposes an edge computing system that enables estimating physical activities of people in offices from time-series point-cloud data, obtained by using a light-detection-and-ranging (LIDAR) sensor network. The paper presents that the proposed system successfully constructs the model for estimating the number of typed characters from time-series point-cloud data, through an experiment using real LIDAR sensors.

READ FULL TEXT

page 1

page 2

research
09/08/2023

Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point Cloud Compression

Real-time light detection and ranging (LiDAR) perceptions, e.g., 3D obje...
research
07/27/2023

FLiCR: A Fast and Lightweight LiDAR Point Cloud Compression Based on Lossy RI

Light detection and ranging (LiDAR) sensors are becoming available on mo...
research
05/22/2019

Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data

Point cloud data from 3D LiDAR sensors are one of the most crucial senso...
research
04/10/2022

Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR

This study shows an enhancement of IoT that gets sensor data and perform...
research
03/05/2021

Hybrid Point Cloud Semantic Compression for Automotive Sensors: A Performance Evaluation

In a fully autonomous driving framework, where vehicles operate without ...
research
12/08/2021

GPCO: An Unsupervised Green Point Cloud Odometry Method

Visual odometry aims to track the incremental motion of an object using ...

Please sign up or login with your details

Forgot password? Click here to reset