YOLO-Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos DOI Creative Commons
Alex Hoi Hang Chan, Prasetia Utama Putra, Harald T. Schupp

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 27, 2024

Abstract Manually coding behaviours from videos is essential to study animal behaviour but it labour-intensive and susceptible inter-rater bias reliability issues. Recent developments of computer vision tools enable the automatic quantification behaviours, supplementing or even replacing manual annotations. However, widespread adoption these methods still limited, due lack annotated training datasets domain-specific knowledge required optimize models for research. Here, we present YOLO-Behaviour, a flexible framework identifying visually distinct video recordings. The robust, easy implement, requires minimal annotations as data. We demonstrate flexibility with case studies event-wise detection in house sparrow nestling provisioning, Siberian jay feeding, human eating frame-wise detections various pigeons, zebras, giraffes. Our results show that reliably detects accurately, retrieve comparable accuracy metrics annotation. extracted were less correlated annotation, potential reasons discrepancy between annotation are discussed. To mitigate this problem, can be used hybrid approach first detecting events using pipeline then manually confirming detections, saving time. provide detailed documentation guidelines on how implement YOLO-Behaviour framework, researchers readily train deploy new their own systems. anticipate another step towards lowering barrier entry applying behaviour.

Language: Английский

YOLO‐Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos DOI Creative Commons
Alex Hoi Hang Chan, Prasetia Utama Putra, Harald T. Schupp

et al.

Methods in Ecology and Evolution, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Abstract Manually coding behaviours from videos is essential to study animal behaviour but it labour‐intensive and susceptible inter‐rater bias reliability issues. Recent developments of computer vision tools enable the automatic quantification behaviours, supplementing or even replacing manual annotation. However, widespread adoption these methods still limited, due lack annotated training datasets domain‐specific knowledge required optimize models for research. Here, we present YOLO‐Behaviour, a flexible framework identifying visually distinct video recordings. The robust, easy implement, requires minimal annotations as data. We demonstrate flexibility with case studies event‐wise detection in house sparrow nestling provisioning, Siberian jay feeding, human eating frame‐wise detections various pigeons, zebras giraffes. Our results show that reliably detects accurately retrieve comparable accuracy metrics extracted were less correlated annotation, potential reasons discrepancy between annotation are discussed. To mitigate this problem, can be used hybrid approach first detecting events using pipeline then manually confirming detections, saving time. provide detailed documentation guidelines on how implement YOLO‐Behaviour framework, researchers readily train deploy new their own systems. anticipate another step towards lowering barrier entry applying behaviour.

Language: Английский

Citations

1
Smart camera traps and computer vision improve detections of small fauna DOI Creative Commons
Angela J. L. Pestell, Anthony R. Rendall, R. Sinclair

et al.

Ecosphere, Journal Year: 2025, Volume and Issue: 16(3)

Published: March 1, 2025

Abstract Limited data on species' distributions are common for small animals, impeding conservation and management. Small especially ectothermic taxa, often difficult to detect, therefore require increased time resources survey effectively. The rise of technology has enabled researchers monitor animals in a range ecosystems longer periods than traditional methods (e.g., live trapping), increasing the quality cost‐effectiveness wildlife monitoring practices. We used DeakinCams, custom‐built smart camera traps, address three aims: (1) To including ectotherms, evaluate performance customized computer vision object detector trained SAWIT dataset automating classification; (2) At same field sites using commercially available we evaluated how well MegaDetector—a freely detection model—detected images containing animals; (3) complementarity these two different approaches monitoring. collected 85,870 videos from DeakinCams 50,888 commercial cameras. For with data, yielded 98% Precision but 47% recall, species classification, varied by 0% Recall birds 26% 14% spiders. detections trap images, MegaDetector returned 99% Recall. found that only detected nocturnal ectotherms invertebrates. Making use more diverse datasets training models as advances machine learning will likely improve like YOLO novel environments. Our results support need continued cross‐disciplinary collaboration ensure large environmental train test existing emerging algorithms.

Language: Английский

Citations

0
BEHAVE - facilitating behaviour coding from videos with AI-detected animals DOI Creative Commons

Reinoud Elhorst,

Martyna Syposz, Katarzyna Wojczulanis‐Jakubas

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103106 - 103106

Published: March 1, 2025

Language: Английский

Citations

0
Not so social in old age: demography as one driver of decreasing sociality DOI Creative Commons
Julia Schroeder, Jamie Dunning, Alvina Chan

et al.

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2024, Volume and Issue: 379(1916)

Published: Oct. 28, 2024

Humans become more selective with whom they spend their time, and as a result, the social networks of older humans are smaller than those younger ones. In non-human animals, processes such competition opportunity can result in patterns declining sociality age. While there is support for age mammals, evidence from wild bird populations lacking. Here, we test whether declines wild, insular population, where know exact ages individuals. Using 6 years data, find that birds aged, degree betweenness decreased. The number same-age still alive also decreased Our results suggest longitudinal change may be, part, an emergent effect natural changes demography. This highlights need to investigate changing costs benefits across lifetime. article part discussion meeting issue ‘Understanding society using populations’.

Language: Английский

Citations

2
YOLO-Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos DOI Creative Commons
Alex Hoi Hang Chan, Prasetia Utama Putra, Harald T. Schupp

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 27, 2024

Abstract Manually coding behaviours from videos is essential to study animal behaviour but it labour-intensive and susceptible inter-rater bias reliability issues. Recent developments of computer vision tools enable the automatic quantification behaviours, supplementing or even replacing manual annotations. However, widespread adoption these methods still limited, due lack annotated training datasets domain-specific knowledge required optimize models for research. Here, we present YOLO-Behaviour, a flexible framework identifying visually distinct video recordings. The robust, easy implement, requires minimal annotations as data. We demonstrate flexibility with case studies event-wise detection in house sparrow nestling provisioning, Siberian jay feeding, human eating frame-wise detections various pigeons, zebras, giraffes. Our results show that reliably detects accurately, retrieve comparable accuracy metrics annotation. extracted were less correlated annotation, potential reasons discrepancy between annotation are discussed. To mitigate this problem, can be used hybrid approach first detecting events using pipeline then manually confirming detections, saving time. provide detailed documentation guidelines on how implement YOLO-Behaviour framework, researchers readily train deploy new their own systems. anticipate another step towards lowering barrier entry applying behaviour.

Language: Английский

Citations

1