Abstract
In Japan, the detection rate of estrus behavior of cattle has declined from 70% to 55% in about 20 years. Causes include the burden of the monitoring system due to the aging of livestock farmers and oversight of detection of estrus behavior by multiple rearing. Because the time period during which estrus behavior appears conspicuously is nearly the same at day and night, it is necessary to monitor on a 24-h system. In the method proposed in this paper, region extraction of black cattle is performed by combining frame difference and MHI (Motion History Image), then feature detection of count formula is performed using the characteristic and features of the riding behaviors. In addition, as a consideration of the model experiment, a method of detecting the riding behavior by combining the vanishing point of the camera and the height from the foot of the cattle was proposed. The effectiveness of both methods were confirmed through experimental results.
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References
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Acknowledgment
This work was supported in part by SCOPE: Strategic Information and Communications R&D Promotion Program (Grant No. 172310006) and JSPS KAKENHI Grant Number 17K08066.
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Hirata, T., Zin, T.T., Kobayashi, I., Hama, H. (2019). A Study on Estrus Detection of Cattle Combining Video Image and Sensor Information. In: Zin, T., Lin, JW. (eds) Big Data Analysis and Deep Learning Applications. ICBDL 2018. Advances in Intelligent Systems and Computing, vol 744. Springer, Singapore. https://doi.org/10.1007/978-981-13-0869-7_30
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DOI: https://doi.org/10.1007/978-981-13-0869-7_30
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