Computer Vision for Detection of Body Posture and Behavior of Red Foxes
The behavior of animals is related to their health and welfare status. The latter plays a particular role in animal experiments, where continuous monitoring is essential for animal welfare. In this study, we focus on red foxes in an experimental setting and study their behavior. Although animal beha...
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| Format: | Article |
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MDPI AG
2022-01-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/12/3/233 |
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| author | Anne K. Schütz E. Tobias Krause Mareike Fischer Thomas Müller Conrad M. Freuling Franz J. Conraths Timo Homeier-Bachmann Hartmut H. K. Lentz |
| author_facet | Anne K. Schütz E. Tobias Krause Mareike Fischer Thomas Müller Conrad M. Freuling Franz J. Conraths Timo Homeier-Bachmann Hartmut H. K. Lentz |
| author_sort | Anne K. Schütz |
| collection | DOAJ |
| description | The behavior of animals is related to their health and welfare status. The latter plays a particular role in animal experiments, where continuous monitoring is essential for animal welfare. In this study, we focus on red foxes in an experimental setting and study their behavior. Although animal behavior is a complex concept, it can be described as a combination of body posture and activity. To measure body posture and activity, video monitoring can be used as a non-invasive and cost-efficient tool. While it is possible to analyze the video data resulting from the experiment manually, this method is time consuming and costly. We therefore use computer vision to detect and track the animals over several days. The detector is based on a neural network architecture. It is trained to detect red foxes and their body postures, i.e., ‘lying’, ‘sitting’, and ‘standing’. The trained algorithm has a mean average precision of 99.91%. The combination of activity and posture results in nearly continuous monitoring of animal behavior. Furthermore, the detector is suitable for real-time evaluation. In conclusion, evaluating the behavior of foxes in an experimental setting using computer vision is a powerful tool for cost-efficient real-time monitoring. |
| first_indexed | 2024-03-10T00:19:07Z |
| format | Article |
| id | doaj.art-9a57e53fc417486eacfe3d6153bb6cd6 |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-10T00:19:07Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-9a57e53fc417486eacfe3d6153bb6cd62023-11-23T15:46:19ZengMDPI AGAnimals2076-26152022-01-0112323310.3390/ani12030233Computer Vision for Detection of Body Posture and Behavior of Red FoxesAnne K. Schütz0E. Tobias Krause1Mareike Fischer2Thomas Müller3Conrad M. Freuling4Franz J. Conraths5Timo Homeier-Bachmann6Hartmut H. K. Lentz7Friedrich-Loeffler-Institut (FLI), Federal Research Institute for Animal Health, Institute of Epidemiology, Südufer 10, 17493 Greifswald-Insel Riems, GermanyFriedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Institute of Animal Welfare and Animal Husbandry, Dörnbergstr. 25/27, 29223 Celle, GermanyInstitute of Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Straße 47, 17487 Greifswald, GermanyFriedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Institute of Molecular Virology and Cell Biology, Südufer 10, 17493 Greifswald-Insel Riems, GermanyFriedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Südufer 10, 17493 Greifswald-Insel Riems, GermanyFriedrich-Loeffler-Institut (FLI), Federal Research Institute for Animal Health, Institute of Epidemiology, Südufer 10, 17493 Greifswald-Insel Riems, GermanyFriedrich-Loeffler-Institut (FLI), Federal Research Institute for Animal Health, Institute of Epidemiology, Südufer 10, 17493 Greifswald-Insel Riems, GermanyFriedrich-Loeffler-Institut (FLI), Federal Research Institute for Animal Health, Institute of Epidemiology, Südufer 10, 17493 Greifswald-Insel Riems, GermanyThe behavior of animals is related to their health and welfare status. The latter plays a particular role in animal experiments, where continuous monitoring is essential for animal welfare. In this study, we focus on red foxes in an experimental setting and study their behavior. Although animal behavior is a complex concept, it can be described as a combination of body posture and activity. To measure body posture and activity, video monitoring can be used as a non-invasive and cost-efficient tool. While it is possible to analyze the video data resulting from the experiment manually, this method is time consuming and costly. We therefore use computer vision to detect and track the animals over several days. The detector is based on a neural network architecture. It is trained to detect red foxes and their body postures, i.e., ‘lying’, ‘sitting’, and ‘standing’. The trained algorithm has a mean average precision of 99.91%. The combination of activity and posture results in nearly continuous monitoring of animal behavior. Furthermore, the detector is suitable for real-time evaluation. In conclusion, evaluating the behavior of foxes in an experimental setting using computer vision is a powerful tool for cost-efficient real-time monitoring.https://www.mdpi.com/2076-2615/12/3/233YOLOv4computer visionanimal monitoringanimal behavioranimal activityanimal welfare |
| spellingShingle | Anne K. Schütz E. Tobias Krause Mareike Fischer Thomas Müller Conrad M. Freuling Franz J. Conraths Timo Homeier-Bachmann Hartmut H. K. Lentz Computer Vision for Detection of Body Posture and Behavior of Red Foxes Animals YOLOv4 computer vision animal monitoring animal behavior animal activity animal welfare |
| title | Computer Vision for Detection of Body Posture and Behavior of Red Foxes |
| title_full | Computer Vision for Detection of Body Posture and Behavior of Red Foxes |
| title_fullStr | Computer Vision for Detection of Body Posture and Behavior of Red Foxes |
| title_full_unstemmed | Computer Vision for Detection of Body Posture and Behavior of Red Foxes |
| title_short | Computer Vision for Detection of Body Posture and Behavior of Red Foxes |
| title_sort | computer vision for detection of body posture and behavior of red foxes |
| topic | YOLOv4 computer vision animal monitoring animal behavior animal activity animal welfare |
| url | https://www.mdpi.com/2076-2615/12/3/233 |
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