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Proceeding Paper

Depth Image Selection Based on Posture for Calf Body Weight Estimation

1
Graduate School of System Informatics, Kobe University, Kobe 657-0013, Japan
2
Graduate School of Science, Technology and Innovation, Kobe University, Kobe 657-8501, Japan
3
Food Resources Education and Research Center, Kobe University, Kasai 675-2103, Japan
4
The Center for Data Science Education and Research, Shiga University, Hikone 522-8522, Japan
*
Author to whom correspondence should be addressed.
Presented at the 13th EFITA International Conference, online, 25–26 May 2021.
Academic Editors: Charisios Achillas and Lefteris Benos
Eng. Proc. 2021, 9(1), 20; https://doi.org/10.3390/engproc2021009020
Published: 25 November 2021
We are developing a system to estimate body weight using calf depth images taken in a loose barn. For this purpose, depth images should be taken from the side, without calves overlapping and without their backs bent. However, most of the depth images that are taken successively and automatically do not satisfy these conditions. Therefore, we need to select only the depth images that match these conditions, as to take many images as possible. The existing method assumes that a calf standing sideways and upright in front of cameras is in a suitable pose. However, since such cases rarely occur, not many images were selected. This paper proposes a new depth image-selection method, focusing on whether a calf is sideways, and the back is not bent, regardless of whether the calf is still or walking. First, depth images including only a single calf are extracted. The calf was identified using radio frequency identification (RFID) when its depth image was taken. Then, the calf area was extracted by background subtraction and contour detection with a depth image. Finally, to judge the usable depth images, we detected and evaluated the calf’s posture, such as the angle of the calf to the camera and the slope of the dorsal line. We used the mean absolute percentage error (MAPE) to assess the efficiency of our method. As two times the number of depth images were extracted, our method achieved an MAPE of 12.45%, while the existing method achieved an MAPE of 13.87%. From this result, we have confirmed that our method makes body weight estimation more accurate. View Full-Text
Keywords: cattle; calf; weight estimation; image processing; depth camera cattle; calf; weight estimation; image processing; depth camera
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    Link: https://github.com/YY0707/EFITA2021_ID239
    Description: Figure S1: Process flow of calf body weight estimation, Figure S2: Example of a bad model of the body when the angle to the camera is inclined, Figure S3: Example of a bad model of the body when the dorsal line slopes, Figure S4: Examples of images with multiple calves overlapping
MDPI and ACS Style

Yamamoto, Y.; Ohkawa, T.; Ohta, C.; Oyama, K.; Nishide, R. Depth Image Selection Based on Posture for Calf Body Weight Estimation. Eng. Proc. 2021, 9, 20. https://doi.org/10.3390/engproc2021009020

AMA Style

Yamamoto Y, Ohkawa T, Ohta C, Oyama K, Nishide R. Depth Image Selection Based on Posture for Calf Body Weight Estimation. Engineering Proceedings. 2021; 9(1):20. https://doi.org/10.3390/engproc2021009020

Chicago/Turabian Style

Yamamoto, Yuki, Takenao Ohkawa, Chikara Ohta, Kenji Oyama, and Ryo Nishide. 2021. "Depth Image Selection Based on Posture for Calf Body Weight Estimation" Engineering Proceedings 9, no. 1: 20. https://doi.org/10.3390/engproc2021009020

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