Smith, Mark and Maiti, Ananda and Maxwell, Andrew and Kist, Alexander 
ORCID: https://orcid.org/0000-0001-9105-7050
(2020)
Colour histogram segmentation for object tracking in remote laboratory environments.
In: 16th International Conference on Remote Engineering
and Virtual Instrumentation: Cyber-Physical Systems and Digital Twins (REV 2019), 3 Feb - 6 Feb 2019, Bangalore, India.
Abstract
Remote Laboratories are online learning environments where a major component of student’s learning objectives is met though visual feedback. This is usually through a static webcam feedback at non-HD resolution. An effective method of enhancing the learning procedure is by tracking certain objects of learning interests in the video feedback. Detecting and tracking moving objects within a video sequence commonly employs varying segmentation methods such as background subtraction to isolate objects of interest. This paper presents two colour histograms models as a method to segment frames from a video sequence and an end-to-end tracking system. Six tests and their results are presented in this paper with varying frame rates and sequencing times.
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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021) |
| Date Deposited: | 14 Mar 2022 07:16 |
| Last Modified: | 16 Mar 2022 01:14 |
| Uncontrolled Keywords: | computer vision; cyber-physical systems; e-learning; image segmentation; remote laboratories |
| Fields of Research (2020): | 40 ENGINEERING > 4010 Engineering practice and education > 401002 Engineering education 46 INFORMATION AND COMPUTING SCIENCES > 4607 Graphics, augmented reality and games > 460799 Graphics, augmented reality and games not elsewhere classified 46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460603 Cyberphysical systems and internet of things |
| Socio-Economic Objectives (2020): | 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220404 Computer systems 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering |
| Identification Number or DOI: | https://doi.org/10.1007/978-3-030-23162-0_49 |
| URI: | http://eprints.usq.edu.au/id/eprint/47148 |
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