Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes

Schütz AK, Schöler  V, Krause T, Fischer  M, Müller  T, Freuling CM, Conraths  FJ, Stanke M, Homeier-Bachmann T, Lentz HHK (2021)
Animals 11(6): 1723.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Schütz, Anne K.; Schöler , Verena; Krause, TobiasUniBi ; Fischer , Mareike; Müller , Thomas; Freuling, Conrad M.; Conraths , Franz J.; Stanke, Mario; Homeier-Bachmann, Timo; Lentz, Hartmut H. K.
Abstract / Bemerkung
Animal activity is an indicator for its welfare and manual observation is time and cost intensive. To this end, automatic detection and monitoring of live captive animals is of major importance for assessing animal activity, and, thereby, allowing for early recognition of changes indicative for diseases and animal welfare issues. We demonstrate that machine learning methods can provide a gap-less monitoring of red foxes in an experimental lab-setting, including a classification into activity patterns. Therefore, bounding boxes are used to measure fox movements, and, thus, the activity level of the animals. We use computer vision, being a non-invasive method for the automatic monitoring of foxes. More specifically, we train the existing algorithm ‘you only look once’ version 4 (YOLOv4) to detect foxes, and the trained classifier is applied to video data of an experiment involving foxes. As we show, computer evaluation outperforms other evaluation methods. Application of automatic detection of foxes can be used for detecting different movement patterns. These, in turn, can be used for animal behavioral analysis and, thus, animal welfare monitoring. Once established for a specific animal species, such systems could be used for animal monitoring in real-time under experimental conditions, or other areas of animal husbandry.
Erscheinungsjahr
2021
Zeitschriftentitel
Animals
Band
11
Ausgabe
6
Art.-Nr.
1723
eISSN
2076-2615
Page URI
https://pub.uni-bielefeld.de/record/2955823

Zitieren

Schütz AK, Schöler  V, Krause T, et al. Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes. Animals. 2021;11(6): 1723.
Schütz, A. K., Schöler , V., Krause, T., Fischer , M., Müller , T., Freuling, C. M., Conraths , F. J., et al. (2021). Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes. Animals, 11(6), 1723. https://doi.org/10.3390/ani11061723
Schütz, A. K., Schöler , V., Krause, T., Fischer , M., Müller , T., Freuling, C. M., Conraths , F. J., Stanke, M., Homeier-Bachmann, T., and Lentz, H. H. K. (2021). Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes. Animals 11:1723.
Schütz, A.K., et al., 2021. Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes. Animals, 11(6): 1723.
A.K. Schütz, et al., “Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes”, Animals, vol. 11, 2021, : 1723.
Schütz, A.K., Schöler , V., Krause, T., Fischer , M., Müller , T., Freuling, C.M., Conraths , F.J., Stanke, M., Homeier-Bachmann, T., Lentz, H.H.K.: Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes. Animals. 11, : 1723 (2021).
Schütz, Anne K., Schöler , Verena, Krause, Tobias, Fischer , Mareike, Müller , Thomas, Freuling, Conrad M., Conraths , Franz J., Stanke, Mario, Homeier-Bachmann, Timo, and Lentz, Hartmut H. K. “Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes”. Animals 11.6 (2021): 1723.

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