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Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence

Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been...

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Autores principales: Boenisch, Franziska, Rosemann, Benjamin, Wild, Benjamin, Dormagen, David, Wario, Fernando, Landgraf, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805663/
https://www.ncbi.nlm.nih.gov/pubmed/33500921
http://dx.doi.org/10.3389/frobt.2018.00035
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author Boenisch, Franziska
Rosemann, Benjamin
Wild, Benjamin
Dormagen, David
Wario, Fernando
Landgraf, Tim
author_facet Boenisch, Franziska
Rosemann, Benjamin
Wild, Benjamin
Dormagen, David
Wario, Fernando
Landgraf, Tim
author_sort Boenisch, Franziska
collection PubMed
description Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been shown to affect and modulate future social interactions. So far, no lifetime history observations have been reported for all bees of a colony. In a previous work we introduced a recording setup customized to track up to 4,000 marked bees over several weeks. Due to detection and decoding errors of the bee markers, linking the correct correspondences through time is non-trivial. In this contribution we present an in-depth description of the underlying multi-step algorithm which produces motion paths, and also improves the marker decoding accuracy significantly. The proposed solution employs two classifiers to predict the correspondence of two consecutive detections in the first step, and two tracklets in the second. We automatically tracked ~2,000 marked honey bees over 10 weeks with inexpensive recording hardware using markers without any error correction bits. We found that the proposed two-step tracking reduced incorrect ID decodings from initially ~13% to around 2% post-tracking. Alongside this paper, we publish the first trajectory dataset for all bees in a colony, extracted from ~3 million images covering 3 days. We invite researchers to join the collective scientific effort to investigate this intriguing animal system. All components of our system are open-source.
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spelling pubmed-78056632021-01-25 Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence Boenisch, Franziska Rosemann, Benjamin Wild, Benjamin Dormagen, David Wario, Fernando Landgraf, Tim Front Robot AI Robotics and AI Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been shown to affect and modulate future social interactions. So far, no lifetime history observations have been reported for all bees of a colony. In a previous work we introduced a recording setup customized to track up to 4,000 marked bees over several weeks. Due to detection and decoding errors of the bee markers, linking the correct correspondences through time is non-trivial. In this contribution we present an in-depth description of the underlying multi-step algorithm which produces motion paths, and also improves the marker decoding accuracy significantly. The proposed solution employs two classifiers to predict the correspondence of two consecutive detections in the first step, and two tracklets in the second. We automatically tracked ~2,000 marked honey bees over 10 weeks with inexpensive recording hardware using markers without any error correction bits. We found that the proposed two-step tracking reduced incorrect ID decodings from initially ~13% to around 2% post-tracking. Alongside this paper, we publish the first trajectory dataset for all bees in a colony, extracted from ~3 million images covering 3 days. We invite researchers to join the collective scientific effort to investigate this intriguing animal system. All components of our system are open-source. Frontiers Media S.A. 2018-04-04 /pmc/articles/PMC7805663/ /pubmed/33500921 http://dx.doi.org/10.3389/frobt.2018.00035 Text en Copyright © 2018 Boenisch, Rosemann, Wild, Dormagen, Wario and Landgraf. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Boenisch, Franziska
Rosemann, Benjamin
Wild, Benjamin
Dormagen, David
Wario, Fernando
Landgraf, Tim
Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence
title Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence
title_full Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence
title_fullStr Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence
title_full_unstemmed Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence
title_short Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence
title_sort tracking all members of a honey bee colony over their lifetime using learned models of correspondence
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805663/
https://www.ncbi.nlm.nih.gov/pubmed/33500921
http://dx.doi.org/10.3389/frobt.2018.00035
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