Analysing Animal Behaviour in Wildlife Videos Using Face Detection and Tracking

This paper presents an algorithm that categorises animal behaviour using detection and tracking of animal faces in wildlife videos. As an example, the algorithm is applied to lion faces. The detection algorithm is based on a human face detection method, utilising Haar-like features and AdaBoost classifiers. The face tracking is implemented using the Kanade-Lucas-Tomasi tracker and by applying a specific interest model to the detected face. By combining the two methods in a specific tracking model, a reliable and temporally coherent detection/tracking of animal faces is achieved. The information generated by the tracker is used to automatically annotate the animal’s behaviour. The annotation classes of locomotive processes for a given