Abstract
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 contribution | Real-time Face Detection and Tracking of Animals |
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Original language | English |
Title of host publication | IEEE 8th Seminar on Neural Network Applications in Electrical Engineering (NEUREL06) |
Pages | 27 - 32 |
Number of pages | 6 |
Publication status | Published - Sep 2006 |
Bibliographical note
Other page information: 27-32Conference Proceedings/Title of Journal: IEEE 8th Seminar on Neural Network Applications in Electrical Engineering (NEUREL06)
Other identifier: 2000637