Machine Learning Recognition of Predator-Induced Vigilance in Wild Ungulates

Abstract

One of the vital survival behaviors exhibited by the wild ungulates is predator-induced vigilance behavior, which allows animals to detect predators and change their behavior accordingly. Understanding predator-prey interactions, habitat adaptations and wildlife management practices can be enhanced through the accurate observation of the predator-induced vigilance behavior. However, the conventional techniques used for recognizing vigilance behaviors in animals depend largely on observational approaches which are tedious, laborious, and dependent on the environmental settings. In this research paper, present a new framework using machine learning techniques for the automatic recognition of predator-induced vigilance behavior in wild ungulates. The suggested method employs the behavioral observations along with visual cues derived from the wildlife monitoring data for classification of various states of vigilance behavior such as posture, scanning, and non-vigilance behaviors. The model framework is comprised of data pre-processing, feature extraction, model training and performance evaluation with the help of classification measures. Experiment results show that the proposed model is able to detect vigilance behavior accurately from normal activities, which is a reliable behavioral assessment model. Using machine learning increases the efficiency, scalability and accuracy of wildlife behavior monitoring, compared to traditional monitoring methods. The proposed model can be used for ecology studies by facilitating continuous observation of ungulates' reactions under predation threats. Future work will concentrate on integrating deep learning models in vision and sensor networks for wildlife behavior study