Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis
The study of the self-protective behaviors of dairy cows suffering dipteral insect infestation is important for evaluating the breeding environment and cows’ selective breeding. The current practices for measuring diary cows’ self-protective behaviors are mostly by human observation, which is not on...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2018/9106836 |
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| _version_ | 1832565089479163904 |
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| author | Jia Li Pei Wu Feilong Kang Lina Zhang Chuanzhong Xuan |
| author_facet | Jia Li Pei Wu Feilong Kang Lina Zhang Chuanzhong Xuan |
| author_sort | Jia Li |
| collection | DOAJ |
| description | The study of the self-protective behaviors of dairy cows suffering dipteral insect infestation is important for evaluating the breeding environment and cows’ selective breeding. The current practices for measuring diary cows’ self-protective behaviors are mostly by human observation, which is not only tedious but also inefficient and inaccurate. In this paper, we develop an automatic monitoring system based on video analysis. First, an improved optical flow tracking algorithm based on Shi-Tomasi corner detection is presented. By combining the morphological features of head, leg, and tail movements, this method effectively reduces the number of Shi-Tomasi points, eliminates interference from background movement, reduces the computational complexity of the algorithm, and improves detection accuracy. The detection algorithm is used to calculate the number of tail, leg, and head movements by using an artificial neural network. The accuracy range of the tail and head reached [0.88, 1] and the recall rate was [0.87, 1]. The method proposed in this paper which provides objective measurements can help researchers to more effectively analyze dairy cows’ self-protective behaviors and the living environment in the process of dairy cow breeding and management. |
| format | Article |
| id | doaj-art-3df3a13fd17144d9bd3329f8e0d2c3af |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-3df3a13fd17144d9bd3329f8e0d2c3af2025-02-03T01:09:26ZengWileyAdvances in Multimedia1687-56801687-56992018-01-01201810.1155/2018/91068369106836Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision AnalysisJia Li0Pei Wu1Feilong Kang2Lina Zhang3Chuanzhong Xuan4College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Engineering Research Center for Intelligent Facilities in Grass and Livestock Breeding, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Engineering Research Center for Intelligent Facilities in Grass and Livestock Breeding, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Engineering Research Center for Intelligent Facilities in Grass and Livestock Breeding, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Engineering Research Center for Intelligent Facilities in Grass and Livestock Breeding, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Engineering Research Center for Intelligent Facilities in Grass and Livestock Breeding, Hohhot 010018, ChinaThe study of the self-protective behaviors of dairy cows suffering dipteral insect infestation is important for evaluating the breeding environment and cows’ selective breeding. The current practices for measuring diary cows’ self-protective behaviors are mostly by human observation, which is not only tedious but also inefficient and inaccurate. In this paper, we develop an automatic monitoring system based on video analysis. First, an improved optical flow tracking algorithm based on Shi-Tomasi corner detection is presented. By combining the morphological features of head, leg, and tail movements, this method effectively reduces the number of Shi-Tomasi points, eliminates interference from background movement, reduces the computational complexity of the algorithm, and improves detection accuracy. The detection algorithm is used to calculate the number of tail, leg, and head movements by using an artificial neural network. The accuracy range of the tail and head reached [0.88, 1] and the recall rate was [0.87, 1]. The method proposed in this paper which provides objective measurements can help researchers to more effectively analyze dairy cows’ self-protective behaviors and the living environment in the process of dairy cow breeding and management.http://dx.doi.org/10.1155/2018/9106836 |
| spellingShingle | Jia Li Pei Wu Feilong Kang Lina Zhang Chuanzhong Xuan Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis Advances in Multimedia |
| title | Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis |
| title_full | Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis |
| title_fullStr | Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis |
| title_full_unstemmed | Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis |
| title_short | Study on the Detection of Dairy Cows’ Self-Protective Behaviors Based on Vision Analysis |
| title_sort | study on the detection of dairy cows self protective behaviors based on vision analysis |
| url | http://dx.doi.org/10.1155/2018/9106836 |
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