movement activity based classification of animal behaviour with an application to data from cheetah (acinonyx jubatus)运动基于活动的动物行为的分类与应用程序数据猎豹(acinonyx jubatus).pdfVIP

Movement Activity Based Classification of Animal Behaviour with an Application to Data from Cheetah (Acinonyx jubatus) ¨ ¨ 1,2 3,4 4 5 Steffen Grunewalder *, Femke Broekhuis , David Whyte Macdonald , Alan Martin Wilson , John Weldon McNutt3, John Shawe-Taylor1,2, Stephen Hailes2,5 1 Computational Statistics and Machine Learning, University College London, London, United Kingdom, 2 Department of Computer Science, University College London, London, United Kingdom, 3 Botswana Predator Conservation Trust, Maun, Botswana, 4 Wildlife Conservation Research Unit (WildCRU), Department of Zoology, University of Oxford, Recanati-Kaplan Centre, Oxford, United Kingdom, 5 Structure and Motion Laboratory, The Royal Veterinary College, University of London, London, United Kingdom Abstract We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal’s behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We dem