Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-20T03:46:59.376Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allwood, M 2008. The Satterthwaite formula for degrees of freedom in two-sample t-test. College Board Advanced Placement Program, AP Statistics. Retrieved October 27, 2014, from http://apcentral.collegeboard.com/apc/public/repository/ap05_stats_allwood_fin4prod. pdf.Google Scholar
Andersen, MR, Jensen, T, Lisouski, P, Mortensen, AK, Hansen, MK, Gregersen, T and Ahrent, P 2012. Kinect depth sensor evaluation for computer vision applications. Technical Report ECE-TR6, Department of Engineering, Aarhus University, Denmark.Google Scholar
Azzaro, G, Caccamo, M, Ferguson, JD, Battiato, S, Farinella, GM, Guarnera, GC, Puglisi, G, Petriglieri, R and Licitra, G 2011. Objective estimation of body condition score by modeling cow body shape from digital images. Journal of Dairy Science 94, 2126–2137.CrossRefGoogle ScholarPubMed
Bercovich, A, Edan, Y, Alcahantis, V, Moallem, U, Parmet, Y, Honig, H, Maltz, E, Antler, A and Halachmi, I 2012. Automatic cow’s body condition scoring. Retrieved July 13, 2013, from cigr.ageng2012.org/images/fotosg/tabla_137_C0565.pdf.Google Scholar
Bergh, J, Ekstedt, F and Lindberg, M 2007. Wavelets mit Anwendungen in Signal- und Bildverarbeitung. Springer, Berlin/Heidelberg, Germany.Google Scholar
Bradley, AP 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30, 1145–1159.CrossRefGoogle Scholar
Cohen, J 1988. Statistical power analysis for the behavioral sciences, 2nd edition. Lawrence Erlbaum Associates, Hillsdale, NJ, USA.Google Scholar
Daubechies, I 1990. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory 36, 961–1005.CrossRefGoogle Scholar
Dudewicz, EJ, Ma, Y, Mai, ES and Su, H 2007. Exact solutions to the Behrens–Fisher Problem: asymptotically optimal and finite sample efficient choice among. Journal of Statistical Planning and Inference 137, 1584–1605.CrossRefGoogle Scholar
Farge, M 1992. Wavelet transforms and their applications to turbulence. Annual Review of Fluid Mechanics 24, 395–457.CrossRefGoogle Scholar
Fawcett, T 2006. An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874.CrossRefGoogle Scholar
Halachmi, I, Klopcic, M, Polak, P, Roberts, DJ and Bewley, JM 2013. Automatic assessment of dairy cattle body condition score using thermal imaging. Computers and Electronics in Agriculture 99, 35–40.CrossRefGoogle Scholar
Hansard, M, Lee, S, Choi, O and Horaud, R 2012. Time-of-flight cameras – principles, methods and applications. Springer, London, England.Google Scholar
Inc. The MathWorks 2007a MATLAB Release Notes. Retrieved March 1, 2010, from www.letu.edu/people/jaytevis/Programming-Languages/MATLAB/Mathworks-Tutorials/16-MATLAB.Google Scholar
Inc. The MathWorks 2007b Statistics toolbox user’s guide, MATLAB. Inc. The MathWorks. Retrieved February 27, 2008, from http://de.mathworks.com/products/statistics.Google Scholar
Kaur, S and Mehra, R 2010. High speed and area efficient 2D dwt processor based image compression. Signal & Image Processing: An International Journal (SIPIJ) 1, 22–31.Google Scholar
Kiencke, U, Schwarz, M and Weickert, T 2008. Signalverarbeitung, Zeit-Frequenz-Analyse und Schätzverfahren, Oldenbourg. Wissenschaftsverlag GmbH, Munich, Germany.CrossRefGoogle Scholar
Krukowski, M 2009. Automatic determination of body condition score of dairy cows from 3D images. Master’s thesis, KTH Computer Science and Communication, Stockholm, Sweden.Google Scholar
Lau, D 2013. The science behind kinects or kinect 1.0 versus 2.0. Retrieved August 22, 2014, from http://www.gamasutra.com/blogs/DanielLau/20131127/205820/ The_Science_Behind_Kinects_or_Kinect_10_versus_20.php.Google Scholar
Louis, AK, Maaß, P and Rieder, A 1998. Wavelets: Theorie und Anwendung, 2nd edition. B.G.Teubner, Stuttgart, Germany.CrossRefGoogle Scholar
Megahed, AI, Moussa, AM, Elrefaie, HB and Marghany, YM 2008. Selection of a suitable mother wavelet for analyzing power system fault transients. IEEE Power and Energy Society General Meeting – Conversion and Delivery of Electrical Energy in the 21st Centruy, 1–7.CrossRefGoogle Scholar
Misiti, M, Misiti, Y, Oppenheim, G and Poggi, JM 2014. Wavelet toolbox user’s guide, MATLAB. The MathWorks Inc. Retrieved November 1, 2014, from http://fr.mathworks.com/help/pdf_doc/wavelet/wavelet_ug.pdf.Google Scholar
Mohd Tumari, SZ, Sudirman, R and Ahmad, AH 2013. Selection of a suitable wavelet for cognitive. Memory Using Electroencephalograph Signal. Engineering 5, 15–19.Google Scholar
OpenNI 2013. The SimpleViewer-example from the OpenNI-project. Retrieved July 31, 2013, from https://github.com/OpenNI.Google Scholar
Pluk, A, Bahr, C, Poursaberi, A, Maertens, W, Van Nuffel, A and Berckmanns, D 2012. Automatic measurement of touch and release angles of the fetlock joint for lameness detection in dairy cattle using vision techniques. Journal of dairy Science 95, 1738–1748.CrossRefGoogle ScholarPubMed
Salau, J, Bauer, U, Haas, JH, Thaller, G, Harms, J and Junge, W 2015. Quantification of the effects of fur, fur color, and velocity on time-of-flight technology in dairy production. SpringerPlus 4, 1–14.CrossRefGoogle ScholarPubMed
Salau, J, Haas, JH, Junge, W, Bauer, U, Harms, J and Bieletzki, S 2014. Feasibility of automated body trait determination using the SR4K time-of-flight camera in cow barns. Springer Plus 3, 1–16.CrossRefGoogle ScholarPubMed
Salau, J, Haas, JH, Thaller, G, Leisen, M and Junge, W 2014. Development of a multi-kinect-system for gait analysis and measuring body characteristics in dairy cows. Proceedings of the EU-PLF, 25 August 2014, Copenhagen, Denmark.Google Scholar
Song, X, Leroy, T, Vranken, E, Maertens, W, Sonck, B and Berckmans, D 2008. Automatic detection of lameness in dairy cattle-vision-based trackway analysis in cow’s locomotion. Computers and Electronics in Agriculture 64, 39–44.CrossRefGoogle Scholar
Szeliski, R 2011. Computer vision: algorithms and applications. Springer, London, England.CrossRefGoogle Scholar
Tucker, CB, Weary, DM and Fraser, D 2004. Free-stall dimensions: effects on preference and stall usage. Journal of Dairy Science 87, 1208–1216.CrossRefGoogle ScholarPubMed
Van Hertem, T, Alchanatis, V, Antler, A, Maltz, E, Halachmi, I, Schlageter-Tello, A, Lokhorst, C, Viazzi, S, Romanini, CEB, Pluk, A, Bahr, C and Berckmans, D 2013. Comparison of segmentation algorithms for cow contour extraction from natural barn background in side view images. Computers and Electronics in Agriculture 91, 65–74.CrossRefGoogle Scholar
Van Hertem, T, Viazzi, S, Steensels, M, Maltz, E, Antler, A, Alchanatis, V, Schlageter-Tello, A, Lokhorst, C, Romanini, CEB, Bahr, C, Berckmans, D and Halachmi, I 2014. Automatic lameness detection based on consecutive 3D-video recordings. Biosystems Engineering 119, 108–116.CrossRefGoogle Scholar
Viazzi, S, Bahr, C, Schlageter-Tello, A, Van Hertem, T, Romanini, CEB, Pluk, A, Halachmi, I, Lokhorst, C and Berckmans, D 2013. Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle. Journal of Dairy Science 96, 257–266.CrossRefGoogle ScholarPubMed
Viazzi, S, Bahr, C, Van Hertem, T, Schlageter-Tello, A, Romanini, CEB, Halachmi, I, Lokhorst, C and Berckmans, D 2014. Comparison of a three-dimensional and a two-dimensional camera system for automated measurement of back posture in dairy cattle. Computers and Electronics in Agriculture 100, 139–147.CrossRefGoogle Scholar
Weber, W, Salau, J, Haas, JH, Junge, W, Bauer, U, Harms, J, Suhr, O, Schönrock, K, Rothfuß, H, Bieletzki, S and Thaller, G 2014. Estimation of backfat thickness using extracted traits from an automatic 3D optical system in lactating Holstein-Friesian cows. Livestock Science 165, 129–137.CrossRefGoogle Scholar
Welch, BL 1947. The generalization of ‘Student’s’ problem when several different population variances are involved. Biometrika 34, 28–35.Google ScholarPubMed
Wilcoxon, F 1945. Individual comparisons by ranking methods. Biometrics Bulletin 1, 80–83.CrossRefGoogle Scholar
Supplementary material: PDF

Salau supplementary material

Figure S2

Download Salau supplementary material(PDF)
PDF 901.3 KB
Supplementary material: PDF

Salau supplementary material

Figure S1

Download Salau supplementary material(PDF)
PDF 1.9 MB

Salau supplementary material

Video

Download Salau supplementary material(Video)
Video 6.5 MB
Supplementary material: File

Salau supplementary material

Salau supplementary material S2

Download Salau supplementary material(File)
File 614.4 KB
Supplementary material: File

Salau supplementary material

Table S1

Download Salau supplementary material(File)
File 25.5 KB
Supplementary material: File

Salau supplementary material

Table S2

Download Salau supplementary material(File)
File 24.7 KB