Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis

TL;DR

This paper presents a novel method for long-term cow tracking in challenging environments, combining localization, segmentation, learning, and tracking to improve accuracy for behavior analysis in precision agriculture.

Contribution

The paper introduces a new integrated approach for cow tracking that addresses environmental challenges and enhances subsequent behavior monitoring capabilities.

Findings

The proposed method outperforms several semi-supervised tracking algorithms.

It demonstrates robustness in cluttered, low-contrast environments.

The approach facilitates early detection of cow lameness.

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 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.