Feasibility study for the implementation of an automatic system for the detection of social interactions in the waiting area of automatic milking stations by using a video surveillance system
Guzhva, O. ; Ardö, H. LU ; Herlin, A. ; Nilsson, M. LU ; Åström, K. LU
and Bergsten, C.
(2016)
In Computers and Electronics in Agriculture
127.
p.506-509
- Abstract
A well-planned waiting area is crucial for automatic milking systems. In an enclosed waiting area, cows of different rank compete for entering the milking station and they are exposed for a variety of social interactions. Such interactions could increase standing time and delay milking, which may result in stress, lameness, impaired welfare and reduced performance. The aim was to monitor the waiting area in a free stall dairy by the use of three video cameras to detect occurrence of social interactions by using improved image segmentation and tracking methods. The surveillance system observed 252 cows having free access to any of four milking stations during 24 h over a period of two weeks. A two-step pattern recognition approach was... (More)
A well-planned waiting area is crucial for automatic milking systems. In an enclosed waiting area, cows of different rank compete for entering the milking station and they are exposed for a variety of social interactions. Such interactions could increase standing time and delay milking, which may result in stress, lameness, impaired welfare and reduced performance. The aim was to monitor the waiting area in a free stall dairy by the use of three video cameras to detect occurrence of social interactions by using improved image segmentation and tracking methods. The surveillance system observed 252 cows having free access to any of four milking stations during 24 h over a period of two weeks. A two-step pattern recognition approach was used. In the first step geometric features (distances) were extracted from every pair of cows in every frame. These features form the input of the second step. It consists of a classifier of the behaviour of the cows. A support vector machine was used to realise this classifier. The social interactions were identified based on collision of geometrical shapes segmented from the image and positively identified as cows by experienced observers. The results showed that the proposed system was capable of a fairly accurate detection of social interactions.
(Less)
- author
- Guzhva, O.
; Ardö, H.
LU
; Herlin, A.
; Nilsson, M.
LU
; Åström, K.
LU
and Bergsten, C.
- organization
- publishing date
- 2016-09-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Computer vision, Dairy cattle, Object recognition, Precision Livestock Farming, Social interactions
- in
- Computers and Electronics in Agriculture
- volume
- 127
- pages
- 4 pages
- publisher
- Elsevier
- external identifiers
-
- wos:000383527100050
- scopus:84978194426
- ISSN
- 0168-1699
- DOI
- 10.1016/j.compag.2016.07.010
- language
- English
- LU publication?
- yes
- id
- 1a3271f7-0576-476a-a9fc-f7fd8610dc1e
- date added to LUP
- 2016-08-29 08:45:47
- date last changed
- 2024-01-04 11:35:26
@article{1a3271f7-0576-476a-a9fc-f7fd8610dc1e,
abstract = {{<p>A well-planned waiting area is crucial for automatic milking systems. In an enclosed waiting area, cows of different rank compete for entering the milking station and they are exposed for a variety of social interactions. Such interactions could increase standing time and delay milking, which may result in stress, lameness, impaired welfare and reduced performance. The aim was to monitor the waiting area in a free stall dairy by the use of three video cameras to detect occurrence of social interactions by using improved image segmentation and tracking methods. The surveillance system observed 252 cows having free access to any of four milking stations during 24 h over a period of two weeks. A two-step pattern recognition approach was used. In the first step geometric features (distances) were extracted from every pair of cows in every frame. These features form the input of the second step. It consists of a classifier of the behaviour of the cows. A support vector machine was used to realise this classifier. The social interactions were identified based on collision of geometrical shapes segmented from the image and positively identified as cows by experienced observers. The results showed that the proposed system was capable of a fairly accurate detection of social interactions.</p>}},
author = {{Guzhva, O. and Ardö, H. and Herlin, A. and Nilsson, M. and Åström, K. and Bergsten, C.}},
issn = {{0168-1699}},
keywords = {{Computer vision; Dairy cattle; Object recognition; Precision Livestock Farming; Social interactions}},
language = {{eng}},
month = {{09}},
pages = {{506--509}},
publisher = {{Elsevier}},
series = {{Computers and Electronics in Agriculture}},
title = {{Feasibility study for the implementation of an automatic system for the detection of social interactions in the waiting area of automatic milking stations by using a video surveillance system}},
url = {{http://dx.doi.org/10.1016/j.compag.2016.07.010}},
doi = {{10.1016/j.compag.2016.07.010}},
volume = {{127}},
year = {{2016}},
}