Automatic livestock body measurement based on keypoint detection with multiple depth cameras

Du, Ao; Guo, Hao; Lü, Jie; Su, Yang; Ma, Qin; Ruchay, Alexey; Marinello, Francesco; Pezzuolo, Andrea

doi:10.1016/j.compag.2022.107059

Cited by 36 publications

(15 citation statements)

References 16 publications

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“…Measurements of exterior features and meat productivity were made using a system of accurate measurement of the body of live cattle using three-depth chambers and non-rigid three-dimensional shape restoration [ 33 , 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

Ruchay

Колпаков

Kosyan

et al. 2022

Animals

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In beef cattle breeding, genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) arrays can reveal many loci of various production traits, such as growth, productivity, and meat quality. With the development of genome sequencing technologies, new opportunities are opening up for more accurate identification of areas associated with these traits. This article aims to develop a novel approach to the lifetime evaluation of cattle by 3-D visualization of economic-biological and genetic features. The purpose of this study was to identify significant variants underlying differences in the qualitative characteristics of meat, using imputed data on the sequence of the entire genome. Samples of biomaterial of young Aberdeen-Angus breed cattle (n = 96) were the material for carrying out genome-wide SNP genotyping. Genotyping was performed using a high-density DNA chip Bovine GPU HD BeadChip (Illumina Inc., San Diego, CA, USA), containing ~150 thousand SNPs. The following indicators were selected as phenotypic features: chest width and chest girth retrieved by 3-D model and meat output on the bones. Correlation analysis showed a reliable positive relationship between chest width and meat output on the bones, which can potentially be used for lifetime evaluation of meat productivity of animals.

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Section: Methodsmentioning

confidence: 99%

Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

Ruchay

Колпаков

Kosyan

et al. 2022

Animals

Self Cite

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“…However, this method cannot standardize all livestock to the same reference pose. Lu et al (2022) [44] improved upon this method by using 2D object detection to determine the orientation of livestock in 3D space. They then projected the 2D detection results into 3D to locate the livestock targets.…”

Section: Posture Judgment and Normalizationmentioning

confidence: 99%

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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“…The height 18,19 of the human body can be measured according to the calculated point cloud map. Du et al 20 introduce a 2D-3D fusion volume measurement method that uses a model to detect anthropometric keypoints on RGB images, combining an interpolation process and a pose normalization method, compared with previous circumference measurement methods, the proposed method is more accurate and robust for cattle data sets. The calculation of the height of the human body is relatively simple.…”

Section: Experimental Datamentioning

confidence: 99%

Research on filtering and measurement algorithms based on human point cloud data

et al. 2022

Int J of Intelligent Sys

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To obtain the data of noncontact measurement of the human body, the depth camera is used to collect the human body, and the obtained initial data are transformed into the required point cloud data for processing through coordinate transformation, and then the collected three‐dimensional point cloud data are preprocessed. The preprocessing includes point cloud downsampling, point cloud filtering, plane segmentation, outlier removal, point cloud surface estimation, and so forth. A new solution for point cloud filtering is proposed, which combines sliding least squares and unification and radius filtering. Compared with the traditional filtering, the effect is smoother, and finally the complete outline of the human body is obtained, and then the human body is measured. The results show that the human body data measured by this scheme is within the range of the relevant standard measurement accuracy.

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Automatic livestock body measurement based on keypoint detection with multiple depth cameras

Cited by 36 publications

References 16 publications

Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

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

Research on filtering and measurement algorithms based on human point cloud data

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