3d model generation of black cattle using multiple rgb cameras for their bcs

Hiroki Tamari, Shohei Nakamura, Shigeru Takano, Yoshihiro Okada

Research output: Chapter in Book/Report/Conference proceedingChapter


This paper presents 3D model generation of black cattle using multiple RGB cameras for their BCS. The use of advanced ICT has a certain possibility to improve various agricultural activities. The authors have such a project whose targets are beef cattle. The goal of the project is to capture 3D shape information of black cattle for the estimation of their body condition scores (BCS). Cattle are always moving because they are animals. Therefore, it is very difficult to capture their body shape information even using a commercial 3D scanner. Another reason is that the color of beef cattle is almost black and then a commercial 3D scanner like a laser range finder cannot be used. So, as the first trial, the authors used multiple RGB cameras to capture RGB images of a cow, generated manually its silhouette images, and employed Shape-from-Silhouette(SfS) method to generate its 3D model. The authors took multiple RGB camera images of cows in a natural environment and generated their 3D models. From the generated 3D models of cows, it can be found that it is possible to estimate the weight of each cow correctly if its accurate silhouette images are generated manually. Here, the problem is how the accurate silhouette images can be obtained automatically in a natural environment. From several experiments, the authors conclude it is impossible. Therefore, the authors propose the use of new method based on multicolor attributed voxels instead of SfS method. This paper clarifies the availability of the new method by showing several experimental results.

Original languageEnglish
Title of host publicationLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
Publication statusPublished - 2018

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Media Technology
  • Computer Science Applications


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