University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Real-time Face Detection and Tracking of Animals

Burghardt, T and Calic, J (2006) Real-time Face Detection and Tracking of Animals In: 8th Seminar on Neural Network Applications in Electrical Engineering, 2006-09-26 - 2006-09-27, Belgrade, Serbia.

[img]
Preview
Text
2006_Burghardt_2006_8th_Seminar_on_Neural_Network_Applications_in_Electrical_Engineering.pdf - ["content_typename_Submitted version (pre-print)" not defined]
Available under License : See the attached licence file.

Download (6MB) | Preview
[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

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 [1], 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 employed 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. © 2006 IEEE

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Burghardt, TUNSPECIFIEDUNSPECIFIED
Calic, JUNSPECIFIEDUNSPECIFIED
Date : 1 January 2006
Identification Number : 10.1109/NEUREL.2006.341167
Contributors :
ContributionNameEmailORCID
PublisherIEEE, UNSPECIFIEDUNSPECIFIED
Related URLs :
Additional Information : © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 02 Dec 2014 12:53
Last Modified : 02 Dec 2014 14:33
URI: http://epubs.surrey.ac.uk/id/eprint/806665

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800