Behaviour Real-Time Spatial Tracking Identification (BeRSTID) used for Cat Behaviour Monitoring in an Animal Shelter

Eagan, Bailey H.; Protopopova, Alexandra

doi:10.1038/s41598-022-22167-3

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…In the current project, we used ArUco markers [ 23 , 24 ], suitable for rapid, low-latency detection of 6D position estimation (3D location and 3D orientation) [ 24 , 25 ]. These markers have been used to track birds [ 26 ], bees [ 27 ] and cats [ 28 ], but to our knowledge this approach has not been applied to cattle.…”

Section: Introductionmentioning

confidence: 99%

Automated monitoring of brush use in dairy cattle

Sadrzadeh,

Foris,

Krahn

et al. 2024

PLoS ONE

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Access to brushes allows for natural scratching behaviors in cattle, especially in confined indoor settings. Cattle are motivated to use brushes, but brush use varies with multiple factors including social hierarchy and health. Brush use might serve an indicator of cow health or welfare, but practical application of these measures requires accurate and automated monitoring tools. This study describes a machine learning approach to monitor brush use by dairy cattle. We aimed to capture the daily brush use by integrating data on the rotation of a mechanical brush with data on cow identify derived from either 1) low-frequency radio frequency identification or 2) a computer vision system using fiducial markers. We found that the computer vision system outperformed the RFID system in accuracy, and that the machine learning algorithms enhanced the precision of the brush use estimates. This study presents the first description of a fiducial marker-based computer vision system for monitoring individual cattle behavior in a group setting; this approach could be applied to develop automated measures of other behaviors with the potential to better assess welfare and improve the care for farm animals.

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Section: Introductionmentioning

confidence: 99%

Automated monitoring of brush use in dairy cattle

Sadrzadeh,

Foris,

Krahn

et al. 2024

PLoS ONE

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“…In parallel with deep learning methods for pose estimation, several unique identifier‐based approaches (Alarcón‐Nieto et al, 2018; Boenisch et al, 2018; Crall et al, 2015; Eagan et al, 2022; Gernat et al, 2018; Mersch et al, 2013) have been developed to maintain the identity of individuals. In these methods, a unique identifier affixed to an individual allows for tracking each individual's position and orientation over time.…”

Section: Introductionmentioning

confidence: 99%

NAPS: Integrating pose estimation and tag‐based tracking

Wolf,

Ruttenberg,

Knapp

et al. 2023

Methods Ecol Evol

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Significant advances in computational ethology have allowed the quantification of behaviour in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both. To capture finely resolved behaviours while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state‐of‐the‐art, deep learning‐based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens). We provide a stand‐alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioural variation within individuals in a group. Expanding the toolkit for capturing the constituent behaviours of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviours and can provide critical data for understanding how individual variation influences collective dynamics.

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“…For instance, Majid et al [5] studied an IoT-based cat feeding and monitoring system, identifying which cat ate the food through RFID tags attached to the cats' collars. Eagan et al [6] researched a computer vision-based marker tracking system for cats in shelters, tracking the behavior of individual cats. They identified the cats by detecting 2D ArUco markers printed on paper collars attached to the cats' necks.…”

Section: Introductionmentioning

confidence: 99%

Multi-Cat Monitoring System Based on Concept Drift Adaptive Machine Learning Architecture

Cho,

Song,

et al. 2023

Sensors

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In multi-cat households, monitoring individual cats’ various behaviors is essential for diagnosing their health and ensuring their well-being. This study focuses on the defecation and urination activities of cats, and introduces an adaptive cat identification architecture based on deep learning (DL) and machine learning (ML) methods. The architecture comprises an object detector and a classification module, with the primary focus on the design of the classification component. The DL object detection algorithm, YOLOv4, is used for the cat object detector, with the convolutional neural network, EfficientNetV2, serving as the backbone for our feature extractor in identity classification with several ML classifiers. Additionally, to address changes in cat composition and individual cat appearances in multi-cat households, we propose an adaptive concept drift approach involving retraining the classification module. To support our research, we compile a comprehensive cat body dataset comprising 8934 images of 36 cats. After a rigorous evaluation of different combinations of DL models and classifiers, we find that the support vector machine (SVM) classifier yields the best performance, achieving an impressive identification accuracy of 94.53%. This outstanding result underscores the effectiveness of the system in accurately identifying cats.

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Behaviour Real-Time Spatial Tracking Identification (BeRSTID) used for Cat Behaviour Monitoring in an Animal Shelter

Cited by 7 publications

References 29 publications

Automated monitoring of brush use in dairy cattle

Automated monitoring of brush use in dairy cattle

NAPS: Integrating pose estimation and tag‐based tracking

Multi-Cat Monitoring System Based on Concept Drift Adaptive Machine Learning Architecture

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