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Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique.

Nilsson, Mikael LU ; Herlin, A H; Ardö, Håkan LU ; Guzhva, O; Åström, Karl LU and Bergsten, C (2015) In Animal 9(11). p.1859-1865
Abstract
In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from... (More)
In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from the image. The learning based approach applied is based on extended state-of-the-art features in combination with a structured prediction framework based on a logistic regression solver using elastic net regularization. In addition, the method is able to produce a probability per pixel rather than form a hard decision. This overcomes some of the limitations found in a setup using grey-scale information only. The pig pen is a difficult imaging environment because of challenging lighting conditions like shadows, poor lighting and poor contrast between pig and background. In order to test practical conditions, a pen containing nine young pigs was filmed from a top view perspective by an Axis M3006 camera with a resolution of 640×480 in three, 10-min sessions under different lighting conditions. The results indicate that a learning based method improves, in comparison with greyscale methods, the possibility to reliable identify proportions of pigs in different areas of the pen. Pigs with a changed behaviour (location) in the pen may indicate changed climate conditions. Changed individual behaviour may also indicate inferior health or acute illness. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
pigs, ratio of areas, image segmentation, behaviour analysis
in
Animal
volume
9
issue
11
pages
1859 - 1865
publisher
Cambridge University Press
external identifiers
  • pmid:26189971
  • wos:000365026200015
  • scopus:84945488967
ISSN
1751-7311
DOI
10.1017/S1751731115001342
language
English
LU publication?
yes
id
febf4a37-34ab-44a8-871e-9da5ef009276 (old id 7749205)
date added to LUP
2015-09-17 14:24:03
date last changed
2017-10-22 03:11:49
@article{febf4a37-34ab-44a8-871e-9da5ef009276,
  abstract     = {In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from the image. The learning based approach applied is based on extended state-of-the-art features in combination with a structured prediction framework based on a logistic regression solver using elastic net regularization. In addition, the method is able to produce a probability per pixel rather than form a hard decision. This overcomes some of the limitations found in a setup using grey-scale information only. The pig pen is a difficult imaging environment because of challenging lighting conditions like shadows, poor lighting and poor contrast between pig and background. In order to test practical conditions, a pen containing nine young pigs was filmed from a top view perspective by an Axis M3006 camera with a resolution of 640×480 in three, 10-min sessions under different lighting conditions. The results indicate that a learning based method improves, in comparison with greyscale methods, the possibility to reliable identify proportions of pigs in different areas of the pen. Pigs with a changed behaviour (location) in the pen may indicate changed climate conditions. Changed individual behaviour may also indicate inferior health or acute illness.},
  author       = {Nilsson, Mikael and Herlin, A H and Ardö, Håkan and Guzhva, O and Åström, Karl and Bergsten, C},
  issn         = {1751-7311},
  keyword      = {pigs,ratio of areas,image segmentation,behaviour analysis},
  language     = {eng},
  number       = {11},
  pages        = {1859--1865},
  publisher    = {Cambridge University Press},
  series       = {Animal},
  title        = {Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique.},
  url          = {http://dx.doi.org/10.1017/S1751731115001342},
  volume       = {9},
  year         = {2015},
}