title: Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique.
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.
authors.affiliation: 1Lund University,Centre for Mathematical Sciences,PO Box 118,SE-22100 Lund,Sweden.
authors.forename: M
authors.initials: M
authors.lastname: Nilsson
authors.affiliation: 2Swedish University of Agricultural Sciences,Department of Biosystems and Technology,PO Box 103,SE-23053 Alnarp,Sweden.
authors.forename: A H
authors.initials: AH
authors.lastname: Herlin
authors.affiliation: 1Lund University,Centre for Mathematical Sciences,PO Box 118,SE-22100 Lund,Sweden.
authors.forename: H
authors.initials: H
authors.lastname: Ardö
authors.affiliation: 2Swedish University of Agricultural Sciences,Department of Biosystems and Technology,PO Box 103,SE-23053 Alnarp,Sweden.
authors.forename: O
authors.initials: O
authors.lastname: Guzhva
authors.affiliation: 1Lund University,Centre for Mathematical Sciences,PO Box 118,SE-22100 Lund,Sweden.
authors.forename: K
authors.initials: K
authors.lastname: Åström
authors.affiliation: 2Swedish University of Agricultural Sciences,Department of Biosystems and Technology,PO Box 103,SE-23053 Alnarp,Sweden.
authors.forename: C
authors.initials: C
authors.lastname: Bergsten
mesh_terms: D000827:Animal Welfare; D000818:Animals; D001185:Artificial Intelligence; D001522:Behavior, Animal; D007091:Image Processing, Computer-Assisted; D016015:Logistic Models; D013552:Swine / Q000502:physiology*
keywords: behaviour analysis; image segmentation; pigs; ratio of areas