@inproceedings{0a6083f3890e4f318f098c57522ad21e,
title = "Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis",
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.",
keywords = "animal behavior, machine learning, machine vision",
author = "Aram Ter-Sarkisov and Robert Ross and John Kelleher",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th Conference on Computer and Robot Vision, CRV 2017 ; Conference date: 17-05-2017 Through 19-05-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/CRV.2017.25",
language = "English",
series = "Proceedings - 2017 14th Conference on Computer and Robot Vision, CRV 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "277--284",
booktitle = "Proceedings - 2017 14th Conference on Computer and Robot Vision, CRV 2017",
address = "United States",
}