Next Article in Journal
A Practical Application Combining Wireless Sensor Networks and Internet of Things: Safety Management System for Tower Crane Groups
Next Article in Special Issue
Relevance-Based Template Matching for Tracking Targets in FLIR Imagery
Previous Article in Journal
Confronting Passive and Active Sensors with Non-Gaussian Statistics
Previous Article in Special Issue
Thermal Tracking of Sports Players
Sensors 2014, 14(8), 13778-13793; doi:10.3390/s140813778
Article

Automated Detection and Recognition of Wildlife Using Thermal Cameras

* ,
,
 and
Department of Engineering, Aarhus University, Finlandsgade 22, Aarhus, Denmark
* Author to whom correspondence should be addressed.
Received: 15 May 2014 / Revised: 1 July 2014 / Accepted: 17 July 2014 / Published: 30 July 2014
View Full-Text   |   Download PDF [9574 KB, uploaded 30 July 2014]   |   Browse Figures

Abstract

In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds of agricultural machinery. Detection and recognition of wildlife within the agricultural fields is important to reduce wildlife mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contributes to the automated detection and classification of animals in thermal imaging. The methods and results are based on top-view images taken manually from a lift to motivate work towards unmanned aerial vehicle-based detection and recognition. Hot objects are detected based on a threshold dynamically adjusted to each frame. For the classification of animals, we propose a novel thermal feature extraction algorithm. For each detected object, a thermal signature is calculated using morphological operations. The thermal signature describes heat characteristics of objects and is partly invariant to translation, rotation, scale and posture. The discrete cosine transform (DCT) is used to parameterize the thermal signature and, thereby, calculate a feature vector, which is used for subsequent classification. Using a k-nearest-neighbor (kNN) classifier, animals are discriminated from non-animals with a balanced classification accuracy of 84.7% in an altitude range of 3–10 m and an accuracy of 75.2% for an altitude range of 10–20 m. To incorporate temporal information in the classification, a tracking algorithm is proposed. Using temporal information improves the balanced classification accuracy to 93.3% in an altitude range 3–10 of meters and 77.7% in an altitude range of 10–20 m
Keywords: thermal imaging; feature extraction; kNN; DCT; pattern recognition thermal imaging; feature extraction; kNN; DCT; pattern recognition
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Export to BibTeX |
EndNote


MDPI and ACS Style

Christiansen, P.; Steen, K.A.; Jørgensen, R.N.; Karstoft, H. Automated Detection and Recognition of Wildlife Using Thermal Cameras. Sensors 2014, 14, 13778-13793.

View more citation formats

Supplement

Related Articles

Article Metrics

Comments

Citing Articles

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
{"cc_re__cc_re__cc_by_3_0": ["creator\" content=\"Kim Steen\">\n            <meta name=\"dc.creator\" content=\"Rasmus J\u00f8rgensen\">\n            <meta name=\"dc.creator\" content=\"Henrik Karstoft\">\n    \n    <meta name=\"dc.type\" content=\"Article\">\n    <meta name=\"dc.source\" content=\"Sensors 2014, Vol. 14, Pages 13778-13793\">\n    <meta name=\"dc.date\" content=\"2014-07-30\">\n            <meta name =\"dc.identifier\" content=\"10.3390/s140813778\">\n    \n    <meta name=\"dc.publisher\" content=\"Multidisciplinary Digital Publishing Institute\">\n    <meta name=\"dc.rights\" content=\"http://creativecommons.org/licenses/by/3.0/\">\n    <meta name=\"dc.format\" content=\"application/pdf\" >\n    <meta name=\"dc.language\" content=\"en\" >\n\t<meta name=\"dc.description\" content=\"In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds of agricultural machinery. Detection and recognition of wildlife within the agricultural fields is important to reduce wildlife mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contri", "tion; kNN; DCT; pattern recognition</span>\t\t  \t<span>\t\t\t  \t\t<a href=\"/search?q=thermal imaging\">thermal imaging</a>;\t\t\t  \t\t<a href=\"/search?q=feature extraction\">feature extraction</a>;\t\t\t  \t\t<a href=\"/search?q=kNN\">kNN</a>;\t\t\t  \t\t<a href=\"/search?q=DCT\">DCT</a>;\t\t\t  \t\t<a href=\"/search?q=pattern recognition\">pattern recognition</a></span>\n\t\t  </div>\n\t\t\t\t\n\n\t\t\n\t\t<div class=\"licenseText in-tab\" style=\"padding: 1em; text-align: justify;\">\n\t\t\t\t<em>This is an open access article distributed under the <a href=\"http://creativecommons.org/licenses/by/3.0/\" target=\"_blank\">\n        Creative Commons Attribution License</a> which permits unrestricted use, distribution,\n\t\tand reproduction in any medium, provided the original work is properly cited.</em>\n\t\t\t\t</div>\n\n   </div>\n   \n   \n   \n   \n    \n   <div id=\"tabs-3\" class=\"ui-tabs-panel\">\n   <h1>Share & Cite This Article</h1>\n\n\t <div class=\"share_div\" style=\"margin: 1.5em 0 0 0em\">\t\t<div class=\"img_btn\"><a href=\"http://twitter.com/home?status=%23mdpisensors+Automated+Detection+and+Recognition+of+Wildlife+Using+Thermal"], "copyright__copyright__copyright": ["ided the original work is properly cited.\n\nShare & Cite This Article\n\nExport to BibTeX |\nEndNote\n\n\nMDPI and ACS Style\n\nChristiansen, P.; Steen, K.A.; J\u00f8rgensen, R.N.; Karstoft, H. Automated Detection and Recognition of Wildlife Using Thermal Cameras. Sensors 2014, 14, 13778-13793.\n\nView more citation formats\n\nSupplement\n\nRelated Articles\n\nArticle Metrics\n\nComments\n\nCiting Articles\n\n[Return to top]\nSensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert"]}