Cow behavior recognition based on image analysis and activities
Keywords:
target segmentation, image entropy, image moment, activities, intelligent analysisAbstract
Abstract: For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video, in this study, 400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall ramp. The method of object recognition based on image entropy was proposed, aiming at the identification of motional cow object behavior against a complex background. Calculating a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back characteristics. Then, by combining the continuous image characteristics and movement of cows for 7 d, the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction, improving the accuracy of the identification of characteristics of dairy cows. Cow behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior identification. The experimental results showed that, through target detection, classification and recognition, the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%, and the false negative rates of oestrus and hoof disease were 3.28% and 5.32%, respectively. This method can enhance the real-time monitoring of cows, save time and improve the management efficiency of large-scale farming. Keywords: cow behavior, target segmentation, image entropy, image moment, activities, intelligent analysis DOI: 10.3965/j.ijabe.20171003.3080 Citation: Gu J Q, Wang Z H, Gao R H, Wu H R. Cow behavior recognition based on image analysis and activities. Int J Agric & Biol Eng, 2017; 10(3): 165–174.References
[1] Mu C, Xie J, Yan W, Liu T, Li P. A fast recognition algorithm for suspicious behavior in high definition videos. Multimedia Systems, 2016; 22(3): 275–285.
[2] Borchers M R, Chang Y M, Tsai I C, Wadsworth B A, Bewley J M. A validation of technologies monitoring dairy cow feeding, ruminating, and lying behaviors. Journal of Dairy Science, 2016; 99(9): 7458–7466.
[3] María B, Marta M, Jorge L A. Behavioral effects in adolescence and early adulthood in two length models of maternal separation in male rats. Behavioural Brain Research, 2017; 324: 77–86.
[4] Rekik K, Francés B, Valet P, Dray C, Florian C. Cognitive deficit in hippocampal-dependent tasks in Werner syndrome mouse model. Behavioural Brain Research, 2017; 323: 68–77.
[5] Devanne M, Berretti S, Pala P, Wannous H, Daoudi M,Bimbo A D. Motion segment decomposition of RGB-D sequences for human behavior understanding. Pattern Recognition, 2017; 61: 222–233.
[6] Salau J, Haas J H, Thaller G, Leisen M, Junge W. Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets. Animal, 2016; 10(9): 1–12.
[7] Wenigera M, Kappa F, Friederichsa P. Spatial verification using wavelet transforms: a review. Quarterly Journal of the Royal Meteorological Society, 2016.
[8] Firk R, Stamer E, Junge W, Krieter J. Automation of
oestrus detection in dairy cows: a review. Livestock Production Science, 2002; 75(3): 219–232.
[9] Batchuluun G, Kim Y G, Kim J H, Hong H G, Park K R. Robust behavior recognition in intelligent surveillance environments. Sensors, 2016; 16(7):1010.
[10] Oh S, Pandey M, Kim I, Hoogs A. Image-oriented economic perspective on user behavior in multimedia social forums. Pattern Recognition Letters, 2016; 72(C): 33–40.
[11] Zissis D, Xidias E K, Lekkas D. Real-time vessel behavior prediction. Evolving Systems, 2016; 7(1): 29–40.
[12] Palacio S, Bergeron R, Lachance S, Vasseur E. The effects of providing portable shade at pasture on dairy cow behavior and physiology. Journal of Dairy Science, 2015; 98(9): 6085–6093.
[13] Sawalhah M N, Cibils A F, Maladi A, Cao H, Vanleeuwen D M. Forage and weather Influence day versus nighttime cow behavior and calf weaning weights on rangeland. Rangeland Ecology & Management, 2016; 69(2): 134–143.
[14] Porto S M C, Arcidiacono C, Anguzza U, Cascone G. The automatic detection of dairy cow feeding and standing behaviours in free-stall barns by a computer vision-based system. Biosystems Engineering, 2015; 133: 46–55.
[15] Servedio M R. The effects of predator learning, forgetting, and recognition errors on the evolution of warning coloration. Evolution, 2015; 54(3): 751–763.
[16] Nilsson M, Herlin A H, Ardö H, Guzhva O, Åström K, Bergsten C. Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique. Animal, 2015; 9(11): 1859–1865.
[17] Cattelan M, Varin C. Hybrid pairwise likelihood analysis of animal behavior experiments. Biometrics, 2013; 69(4): 1002–1011.
[18] Leroy T, Vranken E. A computer vision method for on-line behavioral quantification of individually caged poultry. Transactions of the ASABE, 2006; 49(3): 795–802
[19] Egnor S E, Branson K. Computational analysis of behavior. Annual Review of Neuroscience, 2016; 39(1): 217.
[20] Balch T, Khan Z, Veloso M. Automatically tracking and analyzing the behavior of live insect colonies. AGENTS’01, 2001; pp.521–528.
[21] Shao B, Xin H W. A real-time computer vision assessment and control of the thermal comfort for group-housed pigs. Computer and Electronics in Agriculture, 2008; 62(2): 15–21.
[22] Lao F, Du X D, Teng G H. Automatic recognition method of laying hen behaviors based on depth Image processing. Transactions of the CSAM, 2017; 48(1): 155–162. (in Chinese)
[23] Wang J, Chen X J, Chang L Q, Fang D Y, Xing T Z, Nie W
K.. Compressive sensing based device-free moving target trajectory depiction. Chinese Journal of Computers, 2014; 37(74): 1–15. (in Chinese)
[24] Ji B, Zhu W X, Liu B, Li X, Ma C. Video analysis for tachypnea of pigs based on fluctuating ridge-abdomen. Transactions of the CSAE, 2011; 27(1): 191–195. (in Chinese)
[25] Liu B, Zhu W X, Yang J J, Ma C H. Extracting of pig gait frequency feature based on depth image and pig skeleton endpoints analysis. Transactions of the CSAE, 2014; 30(10): 131–137. (in Chinese)
[26] Zhu W X, Pu X F, Li X C, Lu C F. Automatic identification system of pigs with suspected case based on behavior monitoring. Transactions of the CSAE, 2010; 26(1): 188–192. (in Chinese)
[27] Duan Y, Li D l, Li Z b, Fu Z T. Review on visual attributes measurement research of aquatic animals based on computer vision. Transactions of the CSAE, 2015; 31(15): 1–11. (in Chinese)
[28] Pang C, He D J, Li C Y, Huang C, Zheng L P. Method of traceability information acquisition and transmission for dairy cattle based on integrating of RFID and WSN. Transactions of the CSAE, 2011; 27(9): 147–152. (in Chinese)
[29] He D J, Meng F C, Zhao K X, Zhang Z. Recognition of calf basic behaviors based on video analysis. Transactions of the CSAM, 2016; 47(9): 294–300. (in Chinese)
[30] Tian F Y, Wang R R, Liu M C, Wang Z, Li F D, Wang Z H. Oestrus detection and prediction in dairy cows based on neural networks. Transactions of the CSAM, 2013; 44(10): 277–281. (in Chinese)
[31] Wen C J, Wang S S, Zhao X, Wang M, Ma L, Liu Y T. Visual dictionary for cows sow behavior recognition. Transactions of the CSAM, 2014; 45(1): 266–274. (in Chinese)
[32] Stem U, He R, Yang C H. Analyzing animal behavior via classifying each video frame using convolutional neural networks. Scientific Reforts, 2015; 5: 1–13
[33] Shen M X, Liu L S, Yan L. Review of monitoring technology for animal individual in animal husbandry. Transactions of the CSAM, 2014; 45(10): 245–251. (in Chinese)
[2] Borchers M R, Chang Y M, Tsai I C, Wadsworth B A, Bewley J M. A validation of technologies monitoring dairy cow feeding, ruminating, and lying behaviors. Journal of Dairy Science, 2016; 99(9): 7458–7466.
[3] María B, Marta M, Jorge L A. Behavioral effects in adolescence and early adulthood in two length models of maternal separation in male rats. Behavioural Brain Research, 2017; 324: 77–86.
[4] Rekik K, Francés B, Valet P, Dray C, Florian C. Cognitive deficit in hippocampal-dependent tasks in Werner syndrome mouse model. Behavioural Brain Research, 2017; 323: 68–77.
[5] Devanne M, Berretti S, Pala P, Wannous H, Daoudi M,Bimbo A D. Motion segment decomposition of RGB-D sequences for human behavior understanding. Pattern Recognition, 2017; 61: 222–233.
[6] Salau J, Haas J H, Thaller G, Leisen M, Junge W. Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets. Animal, 2016; 10(9): 1–12.
[7] Wenigera M, Kappa F, Friederichsa P. Spatial verification using wavelet transforms: a review. Quarterly Journal of the Royal Meteorological Society, 2016.
[8] Firk R, Stamer E, Junge W, Krieter J. Automation of
oestrus detection in dairy cows: a review. Livestock Production Science, 2002; 75(3): 219–232.
[9] Batchuluun G, Kim Y G, Kim J H, Hong H G, Park K R. Robust behavior recognition in intelligent surveillance environments. Sensors, 2016; 16(7):1010.
[10] Oh S, Pandey M, Kim I, Hoogs A. Image-oriented economic perspective on user behavior in multimedia social forums. Pattern Recognition Letters, 2016; 72(C): 33–40.
[11] Zissis D, Xidias E K, Lekkas D. Real-time vessel behavior prediction. Evolving Systems, 2016; 7(1): 29–40.
[12] Palacio S, Bergeron R, Lachance S, Vasseur E. The effects of providing portable shade at pasture on dairy cow behavior and physiology. Journal of Dairy Science, 2015; 98(9): 6085–6093.
[13] Sawalhah M N, Cibils A F, Maladi A, Cao H, Vanleeuwen D M. Forage and weather Influence day versus nighttime cow behavior and calf weaning weights on rangeland. Rangeland Ecology & Management, 2016; 69(2): 134–143.
[14] Porto S M C, Arcidiacono C, Anguzza U, Cascone G. The automatic detection of dairy cow feeding and standing behaviours in free-stall barns by a computer vision-based system. Biosystems Engineering, 2015; 133: 46–55.
[15] Servedio M R. The effects of predator learning, forgetting, and recognition errors on the evolution of warning coloration. Evolution, 2015; 54(3): 751–763.
[16] Nilsson M, Herlin A H, Ardö H, Guzhva O, Åström K, Bergsten C. Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique. Animal, 2015; 9(11): 1859–1865.
[17] Cattelan M, Varin C. Hybrid pairwise likelihood analysis of animal behavior experiments. Biometrics, 2013; 69(4): 1002–1011.
[18] Leroy T, Vranken E. A computer vision method for on-line behavioral quantification of individually caged poultry. Transactions of the ASABE, 2006; 49(3): 795–802
[19] Egnor S E, Branson K. Computational analysis of behavior. Annual Review of Neuroscience, 2016; 39(1): 217.
[20] Balch T, Khan Z, Veloso M. Automatically tracking and analyzing the behavior of live insect colonies. AGENTS’01, 2001; pp.521–528.
[21] Shao B, Xin H W. A real-time computer vision assessment and control of the thermal comfort for group-housed pigs. Computer and Electronics in Agriculture, 2008; 62(2): 15–21.
[22] Lao F, Du X D, Teng G H. Automatic recognition method of laying hen behaviors based on depth Image processing. Transactions of the CSAM, 2017; 48(1): 155–162. (in Chinese)
[23] Wang J, Chen X J, Chang L Q, Fang D Y, Xing T Z, Nie W
K.. Compressive sensing based device-free moving target trajectory depiction. Chinese Journal of Computers, 2014; 37(74): 1–15. (in Chinese)
[24] Ji B, Zhu W X, Liu B, Li X, Ma C. Video analysis for tachypnea of pigs based on fluctuating ridge-abdomen. Transactions of the CSAE, 2011; 27(1): 191–195. (in Chinese)
[25] Liu B, Zhu W X, Yang J J, Ma C H. Extracting of pig gait frequency feature based on depth image and pig skeleton endpoints analysis. Transactions of the CSAE, 2014; 30(10): 131–137. (in Chinese)
[26] Zhu W X, Pu X F, Li X C, Lu C F. Automatic identification system of pigs with suspected case based on behavior monitoring. Transactions of the CSAE, 2010; 26(1): 188–192. (in Chinese)
[27] Duan Y, Li D l, Li Z b, Fu Z T. Review on visual attributes measurement research of aquatic animals based on computer vision. Transactions of the CSAE, 2015; 31(15): 1–11. (in Chinese)
[28] Pang C, He D J, Li C Y, Huang C, Zheng L P. Method of traceability information acquisition and transmission for dairy cattle based on integrating of RFID and WSN. Transactions of the CSAE, 2011; 27(9): 147–152. (in Chinese)
[29] He D J, Meng F C, Zhao K X, Zhang Z. Recognition of calf basic behaviors based on video analysis. Transactions of the CSAM, 2016; 47(9): 294–300. (in Chinese)
[30] Tian F Y, Wang R R, Liu M C, Wang Z, Li F D, Wang Z H. Oestrus detection and prediction in dairy cows based on neural networks. Transactions of the CSAM, 2013; 44(10): 277–281. (in Chinese)
[31] Wen C J, Wang S S, Zhao X, Wang M, Ma L, Liu Y T. Visual dictionary for cows sow behavior recognition. Transactions of the CSAM, 2014; 45(1): 266–274. (in Chinese)
[32] Stem U, He R, Yang C H. Analyzing animal behavior via classifying each video frame using convolutional neural networks. Scientific Reforts, 2015; 5: 1–13
[33] Shen M X, Liu L S, Yan L. Review of monitoring technology for animal individual in animal husbandry. Transactions of the CSAM, 2014; 45(10): 245–251. (in Chinese)
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Published
2017-05-31
How to Cite
Jingqiu, G., Zhihai, W., Ronghua, G., & Huarui, W. (2017). Cow behavior recognition based on image analysis and activities. International Journal of Agricultural and Biological Engineering, 10(3), 165–174. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3080
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Information Technology, Sensors and Control Systems
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