Tracking Animals in Wildlife Videos Using Face Detection

T Burghardt; J Calic; B Thomas

Tracking Animals in Wildlife Videos Using Face Detection

T Burghardt, J Calic, B Thomas

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

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 particular 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
Original language	English
Title of host publication	Unknown
Publication status	Published - Oct 2004

Bibliographical note

Conference Proceedings/Title of Journal: European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology

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@inproceedings{37b92a48e69a4e7599089c50a334bcd2,

title = "Tracking Animals in Wildlife Videos Using Face Detection",

abstract = " 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 particular animal species, the information generated by the tracker can be used to boost the priors in the probabilistic semantic classification of wildlife videos. ",

author = "T Burghardt and J Calic and B Thomas",

note = "Conference Proceedings/Title of Journal: European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology",

year = "2004",

month = oct,

language = "English",

booktitle = "Unknown",

}

TY - GEN

T1 - Tracking Animals in Wildlife Videos Using Face Detection

AU - Burghardt, T

AU - Calic, J

AU - Thomas, B

N1 - Conference Proceedings/Title of Journal: European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology

PY - 2004/10

Y1 - 2004/10

N2 - 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 particular animal species, the information generated by the tracker can be used to boost the priors in the probabilistic semantic classification of wildlife videos.

AB - 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 particular animal species, the information generated by the tracker can be used to boost the priors in the probabilistic semantic classification of wildlife videos.

M3 - Conference Contribution (Conference Proceeding)

BT - Unknown

ER -

Tracking Animals in Wildlife Videos Using Face Detection

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