This paper presents an algorithm that categorises animal locomotive behaviour by combining 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 by applying a specific interest model that combines low-level feature tracking with the detection algorithm. 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 locomotive behaviour. The annotation classes of locomotive processes for a given animal species are predefined by a large semantic taxonomy on wildlife domain. The experimental results are presented.
|Translated title of the contribution||Analysing Animal Behaviour in Wildlife Videos Using Face Detection and Tracking|
|Pages (from-to)||305 - 312|
|Number of pages||8|
|Journal||IEE Proceedings - Vision, Image, and Signal Processing|
|Publication status||Published - Jun 2006|