Intelligent monitoring method of cow ruminant behavior based on video analysis technology
Keywords:
dairy cow, rumination, intelligent monitoring, video analysis, animal bahaviorAbstract
To overcome the limitations of traditional dairy cow's rumination detection methods, a video-based analysis on the intelligent monitoring method of cow ruminant behavior was proposed in this study. The Mean Shift algorithm was used to track the jaw motion of dairy cows accurately. The centroid trajectory curve of the cow mouth motion was subsequently extracted from the video. In this way, the monitoring of the ruminant behavior of dairy cows was realized. To verify the accuracy of the method, six videos, a total of 99'00", 24 000 frames were selected. The test results demonstrated that the success rate of this method was 92.03%, despite the interference of behaviors, such as raising or turning of the cow’s head. The results demonstrate that this method, which monitors the ruminant behavior of dairy cows, is effective and feasible. Keywords: dairy cow, rumination, intelligent monitoring, video analysis, animal bahavior DOI: 10.25165/j.ijabe.20171005.3117 Citation: Chen Y J, He D J, Fu Y X, Song H B. Intelligent monitoring method of cow ruminant behavior based on video analysis technology. Int J Agric & Biol Eng, 2017; 10(5): 194–202.References
[1] Yan X, Dong G, Xu W, Liu A, Jose G, Wang Y, et al. Analysis of influence factors on cow’s rumination and activity in Beijing. Acta Veterinaria Et Zootechnica Sinica, 2016; 47(5): 955–961. (in Chinese)
[2] Shao D. Researches on variation of the rumination and its influencing factors in lactating cows. Changchun: Jilin University, 2015. (in Chinese)
[3] Bao Y, Chen X, Zhang L. Analysis of differential diagnosis of cow ruminant disease. Agricultural Development and
Equipments, 2016; 22(1): 164. (in Chinese)
[4] Huang K, Yeh Y, Sun L, Li Y. A simple webcam to record animal behavior. Instrumentation Science and Technology, 2013; 41(6): 619–637.
[5] Davis J D, Darr M J, Xin H, Harmon J D, Russell J R. Development of a GPS herd activity and well-being kit (GPS HAWK) to monitor cattle behavior and the effect of sample interval on travel distance. Applied Engineering in Agriculture, 2011; 27(1): 143–150.
[6] Swain D L, Wilson L A, Dickinson J. Evaluation of an active transponder system to monitor spatial and temporal location of cattle within patches of a grazed sward. Applied Animal Behaviour Science, 2003; 84(3): 185–195.
[7] Rober B, White B J, Renter D G, Larson R L. Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle. Computers and Electronics in Agriculture, 2009; 67(1-2): 80–84.
[8] Yoshitoshi R, Watanabe N, Kawamura K, Sakanoue S, Mizoguchi R, Lee H, et al. Distinguishing cattle foraging activities using an accelerometry-based activity monitor. Rangelands, 2013; 35(3): 28.
[9] White B J, Coetzee J F, Renter D G, Babcock A H, Thomson D U, Andresen D. Evaluation of two-dimensional accelerometers to monitor behavior of beef calves after castration. American Journal of Veterinary Research, 2008; 69(8): 1005–1012.
[10] Ding L, Long R, Yang Y, Xu S. Study on grazing and ruminating behavior of yaks over 24 hours by Iger-recorder in autumn and winter pastures. Actaecologiae Animalis Domastici, 2007; 28(3): 84–89. (in Chinese)
[11] Liu D, Zhao K, He D. Real-time target detection for moving cows based on Gaussian Mixture Model. Transactions of the CSAM, 2016; 47(5): 288–294. (in Chinese)
[12] Zhao K, He D, Wang E. Detection of breathing rate and abnormity of dairy cattle based on video analysis. Transactions of the CSAM, 2014; 45(10): 258–263. (in Chinese)
[13] Yao Y. The research on dairy ruminant information acquisition system based on ANT. Donghua University, 2015. (in Chinese)
[14] Takuji H, Zhang L, He K, Zheng Y, Li Y. Development of radio-telemetry halter for measuring nutritional behavior and its preliminary application on Eld’s deer. ACTA Theriologica Sinica, 2008; 28(4): 417–421. (in Chinese)
[15] Schirmann K, Chapinal N, Weary D M, Heuwieser W, von Keyserlingk M A. Rumination and its relationship to feeding and lying behavior in Holstein dairy cows. Journal of Dairy Science, 2012; 95(6): 3212–3217.
[16] Byskov M. V, Schulze A K, Weisbjerg M R, Markussen B, Nørgaard P. Recording rumination time by a rumination monitoring system in Jersey heifers fed grass clover silage and hay at three feeding levels. Journal of Animal Science, 2014; 92(3): 1110–1118.
[17] Reith S, Brandt H, Hoy S. Simultaneous analysis of activity and rumination time, based on collar-mounted sensor technology, of dairy cows over the peri-estrus period. Livestock Science, 2014; 170: 219–227.
[18] Kamal A H M, Montse P. Translation based estimation technique to handle occlusion while using mean-shift in tracking. Research Journal of Applied Sciences, 2009; 4(4): 129–133.
[19] Chen K, Song K, Kyoungho K, Guo Y. Optimized meanshift target reference model based on improved pixel weighting in visual tracking. Journal of Electronics (China), 2013; 30(3): 283–289.
[20] Rui T, Xin S, Wan Y, Lu R. Meanshift tracking with kalman filter and rotation-invariant features. Applied Mechanics and Materials, 2013; 380(8): 1824–1828.
[21] Ravi K, Sanjana G, Moiz H. Performance analysis of Alpha Beta filter, kalman filter and meanshift for object tracking in video sequences. International Journal of Image, Graphics and Signal Processing, 2015; 7(3): 24–30.
[22] Hou P, Xu J, Zhao J, Zhan X, Fan G. A novel model based on LBP and Meanshift for UAV image segmentation. Applied Mechanics and Materials, 2014; 701(1): 270–273.
[23] Boudhane M, Nsiri B. Object detection and segmentation using adaptive meanshift blob tracking algorithm and graph cuts theory. Image Processing and Communications Challenges 5, 2014; 233: 143–151.
[24] Zhao H. Image denoising algorithm based on multi-scale Meanshift. Journal of Jilin University (Engineering and Technology Edition), 2014; 44(5): 1417–1422. (in Chinese)
[25] Li B, Zeng Z, Chen J. Vehicle classification and tracking based on particle swarm optimization and meanshift. Advanced Materials Research, 2010; 121(1): 417–422.
[26] Jing J, Li G, Li P. The research of automatic registering detection of rotary screen printing machine based on MeanShift and fast Block-Matching algorithm. Journal of Computers, 2012; 7(6): 1369–1376.
[27] Wang X, Wu W, Qian Y. Trajectory clustering based customer movement tracking in a supermarket. CAAI Transactions on Intelligent Systems, 2015; (2): 187–192. (in Chinese)
[28] Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003; 25(5): 564–577.
[2] Shao D. Researches on variation of the rumination and its influencing factors in lactating cows. Changchun: Jilin University, 2015. (in Chinese)
[3] Bao Y, Chen X, Zhang L. Analysis of differential diagnosis of cow ruminant disease. Agricultural Development and
Equipments, 2016; 22(1): 164. (in Chinese)
[4] Huang K, Yeh Y, Sun L, Li Y. A simple webcam to record animal behavior. Instrumentation Science and Technology, 2013; 41(6): 619–637.
[5] Davis J D, Darr M J, Xin H, Harmon J D, Russell J R. Development of a GPS herd activity and well-being kit (GPS HAWK) to monitor cattle behavior and the effect of sample interval on travel distance. Applied Engineering in Agriculture, 2011; 27(1): 143–150.
[6] Swain D L, Wilson L A, Dickinson J. Evaluation of an active transponder system to monitor spatial and temporal location of cattle within patches of a grazed sward. Applied Animal Behaviour Science, 2003; 84(3): 185–195.
[7] Rober B, White B J, Renter D G, Larson R L. Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle. Computers and Electronics in Agriculture, 2009; 67(1-2): 80–84.
[8] Yoshitoshi R, Watanabe N, Kawamura K, Sakanoue S, Mizoguchi R, Lee H, et al. Distinguishing cattle foraging activities using an accelerometry-based activity monitor. Rangelands, 2013; 35(3): 28.
[9] White B J, Coetzee J F, Renter D G, Babcock A H, Thomson D U, Andresen D. Evaluation of two-dimensional accelerometers to monitor behavior of beef calves after castration. American Journal of Veterinary Research, 2008; 69(8): 1005–1012.
[10] Ding L, Long R, Yang Y, Xu S. Study on grazing and ruminating behavior of yaks over 24 hours by Iger-recorder in autumn and winter pastures. Actaecologiae Animalis Domastici, 2007; 28(3): 84–89. (in Chinese)
[11] Liu D, Zhao K, He D. Real-time target detection for moving cows based on Gaussian Mixture Model. Transactions of the CSAM, 2016; 47(5): 288–294. (in Chinese)
[12] Zhao K, He D, Wang E. Detection of breathing rate and abnormity of dairy cattle based on video analysis. Transactions of the CSAM, 2014; 45(10): 258–263. (in Chinese)
[13] Yao Y. The research on dairy ruminant information acquisition system based on ANT. Donghua University, 2015. (in Chinese)
[14] Takuji H, Zhang L, He K, Zheng Y, Li Y. Development of radio-telemetry halter for measuring nutritional behavior and its preliminary application on Eld’s deer. ACTA Theriologica Sinica, 2008; 28(4): 417–421. (in Chinese)
[15] Schirmann K, Chapinal N, Weary D M, Heuwieser W, von Keyserlingk M A. Rumination and its relationship to feeding and lying behavior in Holstein dairy cows. Journal of Dairy Science, 2012; 95(6): 3212–3217.
[16] Byskov M. V, Schulze A K, Weisbjerg M R, Markussen B, Nørgaard P. Recording rumination time by a rumination monitoring system in Jersey heifers fed grass clover silage and hay at three feeding levels. Journal of Animal Science, 2014; 92(3): 1110–1118.
[17] Reith S, Brandt H, Hoy S. Simultaneous analysis of activity and rumination time, based on collar-mounted sensor technology, of dairy cows over the peri-estrus period. Livestock Science, 2014; 170: 219–227.
[18] Kamal A H M, Montse P. Translation based estimation technique to handle occlusion while using mean-shift in tracking. Research Journal of Applied Sciences, 2009; 4(4): 129–133.
[19] Chen K, Song K, Kyoungho K, Guo Y. Optimized meanshift target reference model based on improved pixel weighting in visual tracking. Journal of Electronics (China), 2013; 30(3): 283–289.
[20] Rui T, Xin S, Wan Y, Lu R. Meanshift tracking with kalman filter and rotation-invariant features. Applied Mechanics and Materials, 2013; 380(8): 1824–1828.
[21] Ravi K, Sanjana G, Moiz H. Performance analysis of Alpha Beta filter, kalman filter and meanshift for object tracking in video sequences. International Journal of Image, Graphics and Signal Processing, 2015; 7(3): 24–30.
[22] Hou P, Xu J, Zhao J, Zhan X, Fan G. A novel model based on LBP and Meanshift for UAV image segmentation. Applied Mechanics and Materials, 2014; 701(1): 270–273.
[23] Boudhane M, Nsiri B. Object detection and segmentation using adaptive meanshift blob tracking algorithm and graph cuts theory. Image Processing and Communications Challenges 5, 2014; 233: 143–151.
[24] Zhao H. Image denoising algorithm based on multi-scale Meanshift. Journal of Jilin University (Engineering and Technology Edition), 2014; 44(5): 1417–1422. (in Chinese)
[25] Li B, Zeng Z, Chen J. Vehicle classification and tracking based on particle swarm optimization and meanshift. Advanced Materials Research, 2010; 121(1): 417–422.
[26] Jing J, Li G, Li P. The research of automatic registering detection of rotary screen printing machine based on MeanShift and fast Block-Matching algorithm. Journal of Computers, 2012; 7(6): 1369–1376.
[27] Wang X, Wu W, Qian Y. Trajectory clustering based customer movement tracking in a supermarket. CAAI Transactions on Intelligent Systems, 2015; (2): 187–192. (in Chinese)
[28] Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003; 25(5): 564–577.
Downloads
Published
2017-09-30
How to Cite
Yujuan, C., Dongjian, H., Yinxi, F., & Huaibo, S. (2017). Intelligent monitoring method of cow ruminant behavior based on video analysis technology. International Journal of Agricultural and Biological Engineering, 10(5), 194–202. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3117
Issue
Section
Information Technology, Sensors and Control Systems
License
IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
