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

Automated Detection and Recognition of Wildlife Using Thermal Cameras

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Received: 15 May 2014; in revised form: 1 July 2014 / Accepted: 17 July 2014 / Published: 30 July 2014
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.
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
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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.

AMA Style

Christiansen P, Steen KA, Jørgensen RN, Karstoft H. Automated Detection and Recognition of Wildlife Using Thermal Cameras. Sensors. 2014; 14(8):13778-13793.

Chicago/Turabian Style

Christiansen, Peter; Steen, Kim A.; Jørgensen, Rasmus N.; Karstoft, Henrik. 2014. "Automated Detection and Recognition of Wildlife Using Thermal Cameras." Sensors 14, no. 8: 13778-13793.

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