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Analysing animal behaviour in wildlife videos using face detection and tracking

Burghardt, T and Calic, J (2006) Analysing animal behaviour in wildlife videos using face detection and tracking Vision, Image and Signal Processing, IEE Proceedings -, 153 (3). pp. 305-312.

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Burghardt, T
Calic, J
Date : 2006
DOI : 10.1049/ip-vis:20050052
Uncontrolled Keywords : wildlife videos, face detection, AdaBoost classifiers, object detection, Haar-like features, Haar transforms, feature tracking, biology computing, face tracking, animal locomotive behaviour, zoology, video signal processing, face recognition
Related URLs :
Additional Information : This paper is a postprint of a paper submitted to and accepted for publication in Vision, Image and Signal Processing, IEE Proceedings - and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.
Depositing User : Symplectic Elements
Date Deposited : 10 May 2012 10:32
Last Modified : 31 Oct 2017 14:34

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