@inproceedings{c3adcd8ae548481ab2239c64476bb01b,
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",
pages = "190--195",
editor = "Yang, {Laurence T.} and Fei Hao and Young-Sik Jeong and Park, {James J.}",
booktitle = "Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019",
}