Colour histogram segmentation for object tracking in remote laboratory environments
Paper
Paper/Presentation Title | Colour histogram segmentation for object tracking in remote laboratory environments |
---|---|
Presentation Type | Paper |
Authors | Smith, Mark (Author), Maiti, Ananda (Author), Maxwell, Andrew (Author) and Kist, Alexander (Author) |
Editors | Auer, Michael E. and Ram B., Kalyan |
Journal or Proceedings Title | Lecture Notes in Networks and Systems (Book series) |
ERA Conference ID | 50808 |
Journal Citation | 80, pp. 544-563 |
Number of Pages | 20 |
Year | 2020 |
Publisher | Springer |
Place of Publication | Switzerland |
ISSN | 2367-3370 |
2367-3389 | |
ISBN | 9783030231613 |
9783030231620 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-23162-0_49 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-030-23162-0_49 |
Conference/Event | 16th International Conference on Remote Engineering and Virtual Instrumentation: Cyber-Physical Systems and Digital Twins (REV 2019) |
International Conference on Remote Engineering and Virtual Instrumentation | |
Event Details | 16th International Conference on Remote Engineering
and Virtual Instrumentation: Cyber-Physical Systems and Digital Twins (REV 2019) Event Date 03 to end of 06 Feb 2019 Event Location Bangalore, India |
Event Details | International Conference on Remote Engineering and Virtual Instrumentation REV |
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. |
Keywords | computer vision; cyber-physical systems; e-learning; image segmentation; remote laboratories |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 401002. Engineering education |
460799. Graphics, augmented reality and games not elsewhere classified | |
460603. Cyberphysical systems and internet of things | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Mechanical and Electrical Engineering |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q71qx/colour-histogram-segmentation-for-object-tracking-in-remote-laboratory-environments
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