Convolutional Neural Network for Time Series Cattle Behaviour Classification
Kasirat Turfi Kasfi, Andrew Hellicar, Ashfaqur Rahman · 2016
An experiment is introduced which demonstrates the application of supervised feature learning using a Convolutional Neural Network for cattle behaviour classification. The data set used contains observations from sensors attached to the cattle. Previously this problem was addressed by classifying features learned by a stacked autoencoder. Here we explore an alternative method for learning effective features. Convolutional Neural Networks have shown immense success in computer vision, natural language processing, speech recognition etc. The success of Convolutional Neural Network in so many applications has inspired us to verify how effective this network is on learning features from cattle data. A shallow Convolutional Neural Network such as we have developed in this experiment learns effective features for classification and is time-efficient compared to previous model.