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Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis

Ter-Sarkisov, A., Ross, R. J. and Kelleher, J. D. (2017). Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis. In: 2017 14th Conference on Computer and Robot Vision (CRV). (pp. 277-284). IEEE. ISBN 9781538628188

Abstract

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 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.

Publication Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: machine learning; animal behavior; machine vision
Subjects: G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
S Agriculture > SF Animal culture
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date Deposited: 14 Aug 2020 12:16
URI: https://openaccess.city.ac.uk/id/eprint/24660
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