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Automatic Detection of Cow’s Oestrus in Audio Surveillance System

박대희|장홍희|김석|J. Lee|정용화|S. Oh
아세아·태평양축산학회 (Asian-Australasian Association Of Animal Production Societies)|2013.07|Animal Bioscience|제26권 제7호|pp. 1030-1037|DOI http://dx.doi.org/10.5713/ajas.2012.12628

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참고문헌

키워드

Feature Subset SelectionSound DataSupport Vector Data DescriptionCow’s Oestrus DetectionMel Frequency Cepstrum Coefficient

초록

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.

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.

참고문헌 (39)

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