Analysing animal behaviour in wildlife videos using face detection and tracking
- 1 January 2006
- journal article
- Published by Institution of Engineering and Technology (IET) in IEE Proceedings - Vision, Image, and Signal Processing
- Vol. 153 (3) , 305-312
- https://doi.org/10.1049/ip-vis:20050052
Abstract
An algorithm that categorises animal locomotive behaviour by combining detection and tracking of animal faces in wildlife videos is presented. 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, 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.Keywords
This publication has 5 references indexed in Scilit:
- Automated Person Identification in VideoPublished by Springer Nature ,2004
- An extended set of Haar-like features for rapid object detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Tracking multiple animals in wildlife footagePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Using temporal coherence to build models of animalsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- A semantic event-detection approach and its application to detecting hunts in wildlife videoIEEE Transactions on Circuits and Systems for Video Technology, 2000