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A comparison of autoencoder and statistical features for cattle behaviour classification
conference contribution
posted on 2023-05-23, 11:14 authored by Rahman, A, Smith, D, James HillsJames Hills, Bishop-Hurley, G, Henry, D, Richard RawnsleyRichard RawnsleyA 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.
History
Publication title
Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN)Pagination
1-7Department/School
Tasmanian Institute of Agriculture (TIA)Publisher
Institute of Electrical and Electronics EngineersPlace of publication
United StatesEvent title
2016 International Joint Conference on Neural Networks (IJCNN)Event Venue
Vancouver, CanadaDate of Event (Start Date)
2016-07-24Date of Event (End Date)
2016-07-29Rights statement
Copyright 2016 IEEERepository Status
- Restricted