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.
CITATION STYLE
Smith, M., Maiti, A., Maxwell, A. D., & Kist, A. A. (2020). Colour Histogram Segmentation for Object Tracking in Remote Laboratory Environments. In Lecture Notes in Networks and Systems (Vol. 80, pp. 544–563). Springer. https://doi.org/10.1007/978-3-030-23162-0_49
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