Detecting kangaroos in the wild: the first step towards automated animal surveillance

Zhang, Teng, Wiliem, Arnold, Hemson, Graham and Lovell, Brian C. (2015). Detecting kangaroos in the wild: the first step towards automated animal surveillance. In: 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings. International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Conference, Brisbane, QLD, Australia, (1961-1965). 19-24 April 2015. doi:10.1109/ICASSP.2015.7178313


Author Zhang, Teng
Wiliem, Arnold
Hemson, Graham
Lovell, Brian C.
Title of paper Detecting kangaroos in the wild: the first step towards automated animal surveillance
Conference name International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Conference
Conference location Brisbane, QLD, Australia
Conference dates 19-24 April 2015
Convener IEEE
Proceedings title 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings   Check publisher's open access policy
Journal name ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE (Institute for Electrical and Electronic Engineers)
Publication Year 2015
Year available 2015
Sub-type Fully published paper
DOI 10.1109/ICASSP.2015.7178313
Open Access Status Not yet assessed
ISBN 9781467369978
ISSN 1520-6149
Volume 2015-August
Start page 1961
End page 1965
Total pages 5
Language eng
Formatted Abstract/Summary
Recent studies in computer vision have provided new solutions to real-world problems. In this paper, we focus on using computer vision methods to assist in the study of kangaroos in the wild. In order to investigate the feasibility, we built a kangaroo image dataset from collected data from several national parks across the State of Queensland. To achieve reasonable detection accuracy, we explored a multi-pose approach and proposed a framework based on the state-of-the-art Deformable Part Model (DPM). Experiments show that the proposed framework outperformed the state-of-the-art methods on the proposed dataset. Also, the proposed vision tools are able to help our field biologists in studying kangaroo related problems such as population tracking for activity analysis.
Keyword Object detection
Animal
Kangaroo
Population tracking
DPM
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Tue, 25 Aug 2015, 21:56:54 EST by Anthony Yeates on behalf of School of Information Technol and Elec Engineering