This paper presents an algorithm for 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. In addition to the detection of articular animal species, the information generated by the tracker can be used to boost the priors in the probabilistic semantic classification of wildlife videos.
|Translated title of the contribution||Tracking Animals in Wildlife Videos Using Face Detection|
|Title of host publication||European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology|
|Publication status||Published - Oct 2004|