Learning Based Image Segmentation of Pigs in a Pen
Nilsson, Mikael LU ; Ardö, Håkan LU ; Åström, Karl LU
; Herlin, Anders
; Bergsten, Christer
and Guzhva, Oleksiy
(2014)
Visual observation and analysis of Vertebrate And Insect Behavior 2014
p.1-4
- Abstract
- As farms are getting bigger with more animals,
less manual supervision and attention can be given the animals
on both group and individual level. In order not to jeopardize
animal welfare, automated supervision is in some way already
in use. Function and control of ventilation is already in use in
modern pig stables, e.g. by the use of sensors for temperature,
relative humidity and malfunction connected to alarm. However,
by measuring continuously directly on the pigs, more information
and more possibilities to adjust production inputs would be
possible. In this work, the focus is on a key image processing
algorithm aiding such a continuous system -... (More) - As farms are getting bigger with more animals,
less manual supervision and attention can be given the animals
on both group and individual level. In order not to jeopardize
animal welfare, automated supervision is in some way already
in use. Function and control of ventilation is already in use in
modern pig stables, e.g. by the use of sensors for temperature,
relative humidity and malfunction connected to alarm. However,
by measuring continuously directly on the pigs, more information
and more possibilities to adjust production inputs would be
possible. In this work, the focus is on a key image processing
algorithm aiding such a continuous system - segmentation of pigs
in images from video. The proposed solution utilizes extended
state-of-the-art features in combination with a structured prediction
framework based on a logistic regression solver using elastic
net regularization. Objective results on manually segmented
images indicate that the proposed solution, based on learning,
performs better than approaches suggested in recent publications
addressing pig segmentation in video. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5142540
- author
- Nilsson, Mikael
LU
; Ardö, Håkan
LU
; Åström, Karl
LU
; Herlin, Anders
; Bergsten, Christer
and Guzhva, Oleksiy
- organization
- publishing date
- 2014
- type
- Contribution to conference
- publication status
- published
- subject
- keywords
- Precision Livestock Farming, Machine Learning, Computer Vision
- pages
- 4 pages
- conference name
- Visual observation and analysis of Vertebrate And Insect Behavior 2014
- conference location
- Stockholm, Sweden
- conference dates
- 2014-08-24
- language
- English
- LU publication?
- yes
- additional info
- The paper was presented at a workshop in conjunction with the International Conference on Pattern Recognition (ICPR 2014): http://homepages.inf.ed.ac.uk/rbf/vaib14.html
- id
- c1a453fa-30f4-488f-9461-e87758c689b5 (old id 5142540)
- alternative location
- http://homepages.inf.ed.ac.uk/rbf/VAIB14PAPERS/nilsson.pdf
- date added to LUP
- 2016-04-04 13:58:19
- date last changed
- 2020-12-22 02:15:58
@misc{c1a453fa-30f4-488f-9461-e87758c689b5,
abstract = {{As farms are getting bigger with more animals,<br/><br>
less manual supervision and attention can be given the animals<br/><br>
on both group and individual level. In order not to jeopardize<br/><br>
animal welfare, automated supervision is in some way already<br/><br>
in use. Function and control of ventilation is already in use in<br/><br>
modern pig stables, e.g. by the use of sensors for temperature,<br/><br>
relative humidity and malfunction connected to alarm. However,<br/><br>
by measuring continuously directly on the pigs, more information<br/><br>
and more possibilities to adjust production inputs would be<br/><br>
possible. In this work, the focus is on a key image processing<br/><br>
algorithm aiding such a continuous system - segmentation of pigs<br/><br>
in images from video. The proposed solution utilizes extended<br/><br>
state-of-the-art features in combination with a structured prediction<br/><br>
framework based on a logistic regression solver using elastic<br/><br>
net regularization. Objective results on manually segmented<br/><br>
images indicate that the proposed solution, based on learning,<br/><br>
performs better than approaches suggested in recent publications<br/><br>
addressing pig segmentation in video.}},
author = {{Nilsson, Mikael and Ardö, Håkan and Åström, Karl and Herlin, Anders and Bergsten, Christer and Guzhva, Oleksiy}},
keywords = {{Precision Livestock Farming; Machine Learning; Computer Vision}},
language = {{eng}},
pages = {{1--4}},
title = {{Learning Based Image Segmentation of Pigs in a Pen}},
url = {{https://lup.lub.lu.se/search/files/6249431/5142541.pdf}},
year = {{2014}},
}