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Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets

Published online by Cambridge University Press:  03 February 2016

J. Salau ,
J. H. Haas ,
G. Thaller ,
M. Leisen  and
W. Junge
J. Salau*
Affiliation:
Institute of Animal Breeding and Husbandry, Kiel University, Olshausenstraße 40, 24098 Kiel, Germany TiDa Tier und Daten GmbH, Bosseer Str. 4c, 24259 Westensee/Brux, Germany
J. H. Haas
Affiliation:
Institute of Animal Breeding and Husbandry, Kiel University, Olshausenstraße 40, 24098 Kiel, Germany
G. Thaller
Affiliation:
Institute of Animal Breeding and Husbandry, Kiel University, Olshausenstraße 40, 24098 Kiel, Germany
M. Leisen
Affiliation:
Rinderzucht Schleswig Holstein eG, Rendsburger Str. 178, 24537 Neumünster, Germany
W. Junge
Affiliation:
Institute of Animal Breeding and Husbandry, Kiel University, Olshausenstraße 40, 24098 Kiel, Germany
*
† E-mail: jsalau@tierzucht.uni-kiel.de
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Abstract

Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images’ high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore- or background and of cows’ or persons’ surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69.

Keywords

dairy cattlemonitoring system3D cameraobject recognitionwavelet transform
Type
Research Article
Information
animal , Volume 10 , Supplement 9 , September 2016 , pp. 1513 - 1524
Copyright
© The Animal Consortium 2016 

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