Colour histogram segmentation for object tracking in remote laboratory environments

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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