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Learning Based Image Segmentation of Pigs in a Pen

Nilsson, Mikael; Ardö, Håkan; Åström, Karl; Herlin, Anders Henrik; Bergsten, Christer; Guzhva, Oleksiy


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.

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