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A comparison of autoencoder and statistical features for cattle behaviour classification

Citation

Rahman, A and Smith, D and Hills, J and Bishop-Hurley, G and Henry, D and Rawnsley, R, A comparison of autoencoder and statistical features for cattle behaviour classification, Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), 24-29 July 2016, Vancouver, Canada, pp. 1-7. (2016) [Refereed Conference Paper]


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Copyright Statement

Copyright 2016 IEEE

Official URL: http://www.wcci2016.org/

Abstract

A study is presented comparing the effectiveness of unsupervised feature representations with handcrafted features for cattle behaviour classification. Precision management of cattle requires the interaction of individual animals to be continuously monitored on the farm. Consequently, classifiers are trained to infer the behaviour of the animals using the observations from the sensors that are fitted upon them. Historically, domain knowledge drives the generation of features for cattle behaviour classifiers. When new behaviours are introduced into the system, however, it is often necessary to modify the feature set; this requires additional design and more data. Autoencoders, on the other hand, can skip this design step by learning a common, unsupervised feature representation for training. Whilst stacked autoencoders successfully represent structured data including speech, language and images, deep networks have not been used to model cattle motion. Hence, we investigate using a stacked autoencoder to learn a feature representation for cattle behaviour classification. Experimental results demonstrate that the autoencoder features perform reasonably well in comparison to the statistical features that are selected using prior knowledge of behaviour motion.

Item Details

Item Type:Refereed Conference Paper
Keywords:cattle behaviour classification, autoencoder, time series data mining. precision agriculture
Research Division:Agricultural and Veterinary Sciences
Research Group:Animal Production
Research Field:Animal Management
Objective Division:Animal Production and Animal Primary Products
Objective Group:Livestock Raising
Objective Field:Dairy Cattle
Author:Hills, J (Dr James Hills)
Author:Rawnsley, R (Dr Richard Rawnsley)
ID Code:109989
Year Published:2016
Deposited By:Tasmanian Institute of Agriculture
Deposited On:2016-07-11
Last Modified:2017-05-03
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