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
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(8), 13778-13793; doi:10.3390/s140813778

Automated Detection and Recognition of Wildlife Using Thermal Cameras

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]   |  


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 View Full-Text
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 (CC BY 3.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top