Automated measurement of livestock body based on pose normalisation using statistical shape model

Luo, Xinying; Hu, Yihu; Gao, Zicheng; Guo, Hao; Su, Yang

doi:10.1016/j.biosystemseng.2023.01.016

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…Experiments show that our approach has high accuracy and robustness. Each reconstruction takes about 2 minutes, which is much faster than our previous work (Luo et al, 2023).…”

Section: Resultsmentioning

confidence: 90%

Rapid and Automated Body Measurement of Cattle Based on Statistical Shape Model

Bao,

Lu,

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The current methods of non-contact livestock body measurement directly deal with the low-quality point cloud data of livestock, which have low robustness and lack practicality. On the one hand, the success rate of keypoint detection for livestock body measurement is low. Due to the severe occlusion and noise in the point cloud data, body measurements of some data cannot be performed. On the other hand, the key frames need to be manually selected from the point cloud sequence during processing. Inspired by the work of 3D reconstruction based on animal statistical shape models, we implement the construction and learning of the statistical shape model of real cattle. Given the establishment of the statistical shape model of cattle, a 3D reconstruction and body measurement approach of real cattle based on low-quality point cloud data is proposed. Nine indicators are calculated and the overall estimation MAPE (Mean Absolute Percentage Error) is 10.27%. The whole process of the body measurement algorithm proposed in our paper can be extended to other quadrupeds.

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“…Experiments show that our approach has high accuracy and robustness. Each reconstruction takes about 2 minutes, which is much faster than our previous work (Luo et al, 2023).…”

Section: Resultsmentioning

confidence: 90%

Rapid and Automated Body Measurement of Cattle Based on Statistical Shape Model

Bao,

Lu,

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…This 2D/3D fusion approach addresses the issues caused by noise and missing values in depth camera captures and provides stability and practicality. Luo et al (2023) [7] collected livestock data during free movement. Given the diverse poses of the fitted mesh, they employed a shape statistical model to normalize different poses to a reference pose, thereby mitigating the posture-related measurement errors induced by animal movements.…”

Section: Posture Judgment and Normalizationmentioning

confidence: 99%

“…However, it is only suitable for cattle breeds with short to medium hair; for other cattle breeds, the measurement errors are significantly higher, making it unsuitable for commercial applications. Hu et al (2023) [69] improved the PointNet++ model and divided entire pig point clouds into various parts, such as the head, ears, torso, limbs, and tail. They then used the point clouds from the different parts to locate and calculate key measurement points, effectively eliminating the interference caused by the other parts of the point cloud.…”

Section: Body Dimension Measurement Of Livestock Using Deep Learning ...mentioning

confidence: 99%

“…For cattle body measurement, the overall estimated accuracy is 91.95%, while in pig body measurement, the accuracy is 87.63% 2023 Luo [7] In summary, compared to measurement based on image sensing data, livestock body dimension measurements based on three-dimensional point cloud sensing data generally yield superior results and can encompass more body parts. However, due to the substantial data volume of point clouds and lengthy computation times, real-time commercial collection demands high-performance hardware.…”

Section: D Visual Technologymentioning

confidence: 99%

“…In the realm of information technology being applied to animal husbandry, the technology for measuring the live body dimensions of livestock typically utilizes sensors to acquire livestock data using methods such as scanning and imaging. Subsequently, computer processing is applied to yield the live body dimension measurements of the animals [ 7 ]. This approach is usually non-invasive, mitigating stress reactions in livestock and thus constituting a non-destructive testing method [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey

Ma,

Sun,

et al. 2024

Sensors

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Livestock’s live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.

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