  This paper introduces a new approach to the long-term tracking of an object
in a challenging environment. The object is a cow and the environment is an
enclosure in a cowshed. Some of the key challenges in this domain are a
cluttered background, low contrast and high similarity between moving objects
which greatly reduces the efficiency of most existing approaches, including
those based on background subtraction. Our approach is split into object
localization, instance segmentation, learning and tracking stages. Our solution
is compared to a range of semi-supervised object tracking algorithms and we
show that the performance is strong and well suited to subsequent analysis. We
present our solution as a first step towards broader tracking and behavior
monitoring for cows in precision agriculture with the ultimate objective of
early detection of lameness.
