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This project, titled, An Investigation into the Automatic Behaviour Classification of Penguins, evaluates the combined contribution of pose estimation and behaviour classification as applied to the endangered African penguin. This upload comprises the tutorial document, GUI, annotation data, and video data facilitating this project. Data investigating one, two, and multiple (three) individuals are provided. The one individual case study considers the following behaviours: braying (b), flapping (flap), preening (preen), resting (rest), standing (stand), and walking (walk). The two individual case study considers the following two behaviours: interaction (int) and no interaction (nint). Finally, the multi-individual case study considers the following two behaviours: excitement (ab) and normal (norm). Combined, these provide a collective overview of the data studied in this project and yield insight into applying machine learning to ecology.
Machine Learning, Computer Vision, Behaviour Classification, African Penguins. Ecology, Conservation, Deep Learning, Data Science, Data Analytics.
Machine Learning, Computer Vision, Behaviour Classification, African Penguins. Ecology, Conservation, Deep Learning, Data Science, Data Analytics.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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