Real-time Face Detection and Tracking of Animals

Tilo Burghardt, Calic Janko

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

36 Citations (Scopus)


This paper presents a real-time method for extracting information about the locomotive activity of animals in wildlife videos by detecting and tracking the animals’ faces. As an example application, the system is trained on lions. The underlying detection strategy is based on the concepts used in the Viola-Jones detector, an algorithm that was originally used for human face detection utilising Haar-like features and AdaBoost classifiers. Smooth and accurate tracking is achieved by integrating the detection algorithm with a low-level feature tracker. A specific coherence model that dynamically estimates the likelihood of the actual presence of an animal based on temporal confidence accumulation is mployed to ensure a reliable and temporally continuous detection/tracking capability. The information generated by the tracker can be used to automatically classify and annotate basic locomotive behaviours in wildlife video repositories.
Translated title of the contributionReal-time Face Detection and Tracking of Animals
Original languageEnglish
Title of host publicationIEEE 8th Seminar on Neural Network Applications in Electrical Engineering (NEUREL06)
Pages27 - 32
Number of pages6
Publication statusPublished - Sep 2006

Bibliographical note

Other page information: 27-32
Conference Proceedings/Title of Journal: IEEE 8th Seminar on Neural Network Applications in Electrical Engineering (NEUREL06)
Other identifier: 2000637


Dive into the research topics of 'Real-time Face Detection and Tracking of Animals'. Together they form a unique fingerprint.

Cite this