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Colour Histogram Segmentation for Object Tracking in Remote Laboratory Environments

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 80)

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
  • Image segmentation
  • E-learning
  • Remote laboratories
  • Cyber-physical systems

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Correspondence to Ananda Maiti .

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Smith, M., Maiti, A., Maxwell, A.D., Kist, A.A. (2020). Colour Histogram Segmentation for Object Tracking in Remote Laboratory Environments. In: Auer, M., Ram B., K. (eds) Cyber-physical Systems and Digital Twins. REV2019 2019. Lecture Notes in Networks and Systems, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-23162-0_49

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