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http://dx.doi.org/10.5713/ajas.2012.12628

Automatic Detection of Cow's Oestrus in Audio Surveillance System  

Chung, Y. (Department of Computer and Information Science, College of Science and Technology, Korea University)
Lee, J. (Department of Computer and Information Science, College of Science and Technology, Korea University)
Oh, S. (Department of Computer and Information Science, College of Science and Technology, Korea University)
Park, D. (Department of Computer and Information Science, College of Science and Technology, Korea University)
Chang, H.H. (Department of Animal Science, Institute of Agriculture & Life Sciences, College of Agriculture and Life Sciences, Gyeongsang National University)
Kim, S. (College of Veterinary Medicine, Gyeongsang National University)
Publication Information
Asian-Australasian Journal of Animal Sciences / v.26, no.7, 2013 , pp. 1030-1037 More about this Journal
Abstract
Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea). In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone) and accurately (over 94% accuracy), either as a standalone solution or to complement known methods.
Keywords
Cow's Oestrus Detection; Sound Data; Mel Frequency Cepstrum Coefficient; Feature Subset Selection; Support Vector Data Description;
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