Segmentation of sows in farrowing pens

The correct segmentation of a foreground object in video recordings is an important task for many surveillance systems. The development of an effective and practical algorithm to segment sows in grayscale video recordings captured under commercial production conditions is described. The segmentation algorithm combines a modified adaptive Gaussian mixture model for background subtraction with the boundaries of foreground objects, which is obtained by using dyadic wavelet transform. This algorithm can accurately extract the shapes of a sow under complex environments, such as dynamic background and illumination changes as well as motionless foreground objects. About 97% of the segmented binary images in the validation data sets can be used to track sow behaviours, such as position, orientation and movement. The experimental results demonstrate that the proposed algorithm is able to provide a basis to detect and classify the sow behaviours in farrowing pens.

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