Behavior Prediction for Ochotona curzoniae Based on Wavelet Neural Network

Abstract

Ochotona curzoniae is one of the main biological disasters in the Qinghai-Tibet plateau and adjacent areas in China. Video-based animal behavior analysis is a critical and fascinating problem for both biologists and computer vision scientists. The behavior prediction for Ochotona curzoniae is a basis of Ochotona curzoniae behavior analysis in video recordings. In this paper, a three-layer wavelet neural network is proposed for short-term Ochotona curzoniae behavior prediction. A commonly used Morlet wavelet has been chosen as the activation function for hidden-layer neurons in the feed-forward neural network. In order to demonstrate the effectiveness of the proposed approach, short-term prediction of Ochotona curzoniae behavior in the natural habitat environment is performed, and we analyze the influence on prediction accuracy at various numbers of input neurons. The forecasted results clearly show that wavelet neural network has good prediction properties for Ochotona curzoniae behavior prediction compared with BP neural network. The model can assist biologists and computer vision scientists to create an effective animal behavior analysis method. The principle of our Ochotona curzoniae behavior prediction used wavelet neural network is helpful to other animal behavior prediction and analysis in video recordings.