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

Diego Monteiro; Jialin Wang; Hai Ning Liang; Nilufar Baghaei; Andrew Abel

doi:10.1007/978-981-32-9244-4_27

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

Department of Computing

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-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 language	English
Title of host publication	Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019
Editors	Laurence T. Yang, Fei Hao, Young-Sik Jeong, James J. Park
Publisher	Springer Verlag
Pages	190-195
Number of pages	6
ISBN (Print)	9789813292437
DOIs	https://doi.org/10.1007/978-981-32-9244-4_27
Publication status	Published - 2020
Event	13th 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 2019 → 26 Apr 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	590
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019
Country/Territory	China
City	Xian
Period	24/04/19 → 26/04/19

Keywords

Classification
Point Cloud
Spatiotemporal data
Video

Access to Document

10.1007/978-981-32-9244-4_27

Cite this

Monteiro, D., Wang, J., Liang, H. N., Baghaei, N., & Abel, A. (2020). SwarMotion: A 3D Point Cloud Video Recording Tool for Classification Purposes. In L. T. Yang, F. Hao, Y.-S. Jeong, & J. J. Park (Eds.), Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019 (pp. 190-195). (Lecture Notes in Electrical Engineering; Vol. 590). Springer Verlag. https://doi.org/10.1007/978-981-32-9244-4_27

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title = "SwarMotion: A 3D Point Cloud Video Recording Tool for Classification Purposes",

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.",

keywords = "Classification, Point Cloud, Spatiotemporal data, Video",

author = "Diego Monteiro and Jialin Wang and Liang, {Hai Ning} and Nilufar Baghaei and Andrew Abel",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019 ; Conference date: 24-04-2019 Through 26-04-2019",

year = "2020",

doi = "10.1007/978-981-32-9244-4_27",

language = "English",

isbn = "9789813292437",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

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editor = "Yang, {Laurence T.} and Fei Hao and Young-Sik Jeong and Park, {James J.}",

booktitle = "Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019",

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Monteiro, D, Wang, J, Liang, HN, Baghaei, N & Abel, A 2020, SwarMotion: A 3D Point Cloud Video Recording Tool for Classification Purposes. in LT Yang, F Hao, Y-S Jeong & JJ Park (eds), Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019. Lecture Notes in Electrical Engineering, vol. 590, Springer Verlag, pp. 190-195, 13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019, Xian, China, 24/04/19. https://doi.org/10.1007/978-981-32-9244-4_27

SwarMotion: A 3D Point Cloud Video Recording Tool for Classification Purposes. / Monteiro, Diego; Wang, Jialin; Liang, Hai Ning et al.
Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019. ed. / Laurence T. Yang; Fei Hao; Young-Sik Jeong; James J. Park. Springer Verlag, 2020. p. 190-195 (Lecture Notes in Electrical Engineering; Vol. 590).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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T1 - SwarMotion

T2 - 13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019

AU - Monteiro, Diego

AU - Wang, Jialin

AU - Liang, Hai Ning

AU - Baghaei, Nilufar

AU - Abel, Andrew

PY - 2020

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AB - 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.

KW - Classification

KW - Point Cloud

KW - Spatiotemporal data

KW - Video

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

Abstract

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Keywords

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