Document Type

Conference Paper

Rights

This item is available under a Creative Commons License for non-commercial use only

Disciplines

2. ENGINEERING AND TECHNOLOGY

Publication Details

15th Conference on Computer and Robot Vision Toronto, Ontario

May 9-11, 2018‍‍‍‍‍‍

Abstract

This paper introduces a new approach to the longterm 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 benchmarked against 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.

DOI

10.1109/CRV.2017.25

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