Classification of Motion Behaviour of Animals using Supervised Learning Algorithms

Flohil, René (2018) Classification of Motion Behaviour of Animals using Supervised Learning Algorithms. Bachelor's Thesis, Artificial Intelligence.

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Abstract

Recognition of the world around us becomes more and more important in both entertainment and practical fields, the interest for research into recognition algorithms also has increased. Few studies have investigated the classification of behaviours of a given animal using machine learning algorithms. This thesis attempts to describe and compare the performance of two different feature detectors: Histogram of Oriented Gradients (HOG) and Image Pixel Intensity (IMG), and two different machine learning algorithms: a Support Vector Machine (SVM) and a Multi-Layer Perceptron (MLP) for recognizing the motion behaviours of goats. The results show that the algorithm IMG + MLP yields better performances than using a combination of HOG + SVM on a smaller train set. This indicates that raw intensity information matters more than using a HOG representation. However, on smaller test samples, all of the algorithms performed exceptionally well attaining a near perfect and similar performance level. The use of HOG + MLP yield better performance than IMG + MLP on a more diverse test set.

Item Type:	Thesis (Bachelor's Thesis)
Supervisor name:	Okafor, E.
Degree programme:	Artificial Intelligence
Thesis type:	Bachelor's Thesis
Language:	English
Date Deposited:	30 Jul 2018
Last Modified:	30 Jul 2018 13:45
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/18142

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