Colour histogram segmentation for object tracking in remote laboratory environments
Paper
Paper/Presentation Title | Colour histogram segmentation for object tracking in remote laboratory environments |
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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 Engineeringand Virtual Instrumentation (REV 2019) |
International Conference on Remote Engineering and Virtual Instrumentation | |
Event Details | 16th International Conference on Remote Engineeringand Virtual Instrumentation (REV 2019) Parent International Conference on Remote Engineering and Virtual Instrumentation Delivery In person 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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