Semantic Keypoint Extraction for Scanned Animals using Multi-Depth-Camera Systems

Falque, Raphael; Vidal-Calleja, Teresa; Alempijevic, Alen

doi:10.1109/icra48891.2023.10160307

Cited by 4 publications

(1 citation statement)

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“…As described in [20], the key points extraction was proposed as a regression problem. Specifically, the distance between each point in the point cloud and each annotated point was computed, yielding m distance vectors, each with a size of n. Utilizing these distances, the key points can be determined by identifying the point with the minimum value in each distance vector.…”

Section: Automated Measurement and Analysis Of Dairy Cows Via 3d Poin...mentioning

confidence: 99%

Utilizing 3D Point Cloud Technology with Deep Learning for Automated Measurement and Analysis of Dairy Cows

Lee,

Alam

et al. 2024

Sensors

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This paper introduces an approach to the automated measurement and analysis of dairy cows using 3D point cloud technology. The integration of advanced sensing techniques enables the collection of non-intrusive, precise data, facilitating comprehensive monitoring of key parameters related to the health, well-being, and productivity of dairy cows. The proposed system employs 3D imaging sensors to capture detailed information about various parts of dairy cows, generating accurate, high-resolution point clouds. A robust automated algorithm has been developed to process these point clouds and extract relevant metrics such as dairy cow stature height, rump width, rump angle, and front teat length. Based on the measured data combined with expert assessments of dairy cows, the quality indices of dairy cows are automatically evaluated and extracted. By leveraging this technology, dairy farmers can gain real-time insights into the health status of individual cows and the overall herd. Additionally, the automated analysis facilitates efficient management practices and optimizes feeding strategies and resource allocation. The results of field trials and validation studies demonstrate the effectiveness and reliability of the automated 3D point cloud approach in dairy farm environments. The errors between manually measured values of dairy cow height, rump angle, and front teat length, and those calculated by the auto-measurement algorithm were within 0.7 cm, with no observed exceedance of errors in comparison to manual measurements. This research contributes to the burgeoning field of precision livestock farming, offering a technological solution that not only enhances productivity but also aligns with contemporary standards for sustainable and ethical animal husbandry practices.

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Section: Automated Measurement and Analysis Of Dairy Cows Via 3d Poin...mentioning

confidence: 99%