Convolutional neural network-based cow interaction watchdog
(2018) In IET Computer Vision 12(2). p.171-177- Abstract
In the field of applied animal behaviour, video recordings of a scene of interest are often made and then evaluated by experts. This evaluation is based on different criteria (number of animals present, an occurrence of certain interactions, the proximity between animals and so forth) and aims to filter out video sequences that contain irrelevant information. However, such task requires a tremendous amount of time and resources, making manual approach ineffective. To reduce the amount of time the experts spend on watching the uninteresting video, this study introduces an automated watchdog system that can discard some of the recorded video material based on user-defined criteria. A pilot study on cows was made where a convolutional... (More)
In the field of applied animal behaviour, video recordings of a scene of interest are often made and then evaluated by experts. This evaluation is based on different criteria (number of animals present, an occurrence of certain interactions, the proximity between animals and so forth) and aims to filter out video sequences that contain irrelevant information. However, such task requires a tremendous amount of time and resources, making manual approach ineffective. To reduce the amount of time the experts spend on watching the uninteresting video, this study introduces an automated watchdog system that can discard some of the recorded video material based on user-defined criteria. A pilot study on cows was made where a convolutional neural network detector was used to detect and count the number of cows in the scene as well as include distances and interactions between cows as filtering criteria. This approach removed 38% (50% for additional filter parameters) of the recordings while only losing 1% (4%) of the potentially interesting video frames.
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- author
- Ardö, Hakan LU ; Guzhva, Oleksiy ; Nilsson, Mikael LU and Herlin, Anders H.
- organization
- publishing date
- 2018-03-01
- type
- Contribution to journal
- publication status
- published
- subject
- in
- IET Computer Vision
- volume
- 12
- issue
- 2
- pages
- 7 pages
- publisher
- Institution of Engineering and Technology
- external identifiers
-
- scopus:85042857889
- ISSN
- 1751-9632
- DOI
- 10.1049/iet-cvi.2017.0077
- language
- English
- LU publication?
- yes
- id
- 407c49c4-76f1-4704-a922-18fa70829808
- date added to LUP
- 2018-03-16 09:59:30
- date last changed
- 2022-04-25 06:10:55
@article{407c49c4-76f1-4704-a922-18fa70829808,
abstract = {{<p>In the field of applied animal behaviour, video recordings of a scene of interest are often made and then evaluated by experts. This evaluation is based on different criteria (number of animals present, an occurrence of certain interactions, the proximity between animals and so forth) and aims to filter out video sequences that contain irrelevant information. However, such task requires a tremendous amount of time and resources, making manual approach ineffective. To reduce the amount of time the experts spend on watching the uninteresting video, this study introduces an automated watchdog system that can discard some of the recorded video material based on user-defined criteria. A pilot study on cows was made where a convolutional neural network detector was used to detect and count the number of cows in the scene as well as include distances and interactions between cows as filtering criteria. This approach removed 38% (50% for additional filter parameters) of the recordings while only losing 1% (4%) of the potentially interesting video frames.</p>}},
author = {{Ardö, Hakan and Guzhva, Oleksiy and Nilsson, Mikael and Herlin, Anders H.}},
issn = {{1751-9632}},
language = {{eng}},
month = {{03}},
number = {{2}},
pages = {{171--177}},
publisher = {{Institution of Engineering and Technology}},
series = {{IET Computer Vision}},
title = {{Convolutional neural network-based cow interaction watchdog}},
url = {{http://dx.doi.org/10.1049/iet-cvi.2017.0077}},
doi = {{10.1049/iet-cvi.2017.0077}},
volume = {{12}},
year = {{2018}},
}