SwarMotion: A 3D Point Cloud Video Recording Tool for Classification Purposes

Diego Monteiro, Jialin Wang, Hai Ning Liang*, Nilufar Baghaei, Andrew Abel

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)

Abstract

Modern life is ever more reliant on computers being able to classify the world around them and computer vision is one of the ways computers do it. Nowadays, due to the advent of reliable and low-cost range sensors like Kinect which provide useful 3D data to feed prediction systems with a new dimension of useful information, computer vision is taking a new step with research demonstrating the potential that this kind of data has. However, very little research has been done using spatiotemporal Point Cloud data (PC-Videos). One reason might be the lack of datasets containing PC-Videos. In this paper, we propose SwarMotion, a multimodal recording tool focused on the acquisition of PC-Videos.

Original languageEnglish
Title of host publicationAdvanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019
EditorsLaurence T. Yang, Fei Hao, Young-Sik Jeong, James J. Park
PublisherSpringer Verlag
Pages190-195
Number of pages6
ISBN (Print)9789813292437
DOIs
Publication statusPublished - 2020
Event13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019 - Xian, China
Duration: 24 Apr 201926 Apr 2019

Publication series

NameLecture Notes in Electrical Engineering
Volume590
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019
Country/TerritoryChina
CityXian
Period24/04/1926/04/19

Keywords

  • Classification
  • Point Cloud
  • Spatiotemporal data
  • Video

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