Literature DB >> 28057825

Machine vision methods for analyzing social interactions.

Alice A Robie1, Kelly M Seagraves1, S E Roian Egnor2, Kristin Branson2.   

Abstract

Recent developments in machine vision methods for automatic, quantitative analysis of social behavior have immensely improved both the scale and level of resolution with which we can dissect interactions between members of the same species. In this paper, we review these methods, with a particular focus on how biologists can apply them to their own work. We discuss several components of machine vision-based analyses: methods to record high-quality video for automated analyses, video-based tracking algorithms for estimating the positions of interacting animals, and machine learning methods for recognizing patterns of interactions. These methods are extremely general in their applicability, and we review a subset of successful applications of them to biological questions in several model systems with very different types of social behaviors.
© 2017. Published by The Company of Biologists Ltd.

Entities:  

Keywords:  Animal behavior; Computer vision; Machine learning; Social behavior

Mesh:

Year:  2017        PMID: 28057825     DOI: 10.1242/jeb.142281

Source DB:  PubMed          Journal:  J Exp Biol        ISSN: 0022-0949            Impact factor:   3.312


  33 in total

1.  A data-driven method for reconstructing and modelling social interactions in moving animal groups.

Authors:  R Escobedo; V Lecheval; V Papaspyros; F Bonnet; F Mondada; C Sire; G Theraulaz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-07-27       Impact factor: 6.237

2.  Ventral hippocampal projections to the medial prefrontal cortex regulate social memory.

Authors:  Mary L Phillips; Holly Anne Robinson; Lucas Pozzo-Miller
Journal:  Elife       Date:  2019-05-21       Impact factor: 8.140

Review 3.  Rage Against the Machine: Advancing the study of aggression ethology via machine learning.

Authors:  Nastacia L Goodwin; Simon R O Nilsson; Sam A Golden
Journal:  Psychopharmacology (Berl)       Date:  2020-07-09       Impact factor: 4.530

Review 4.  A review of 28 free animal-tracking software applications: current features and limitations.

Authors:  Veronica Panadeiro; Alvaro Rodriguez; Jason Henry; Donald Wlodkowic; Magnus Andersson
Journal:  Lab Anim (NY)       Date:  2021-07-29       Impact factor: 12.625

5.  Twitches, Blinks, and Fidgets: Important Generators of Ongoing Neural Activity.

Authors:  Patrick J Drew; Aaron T Winder; Qingguang Zhang
Journal:  Neuroscientist       Date:  2018-10-12       Impact factor: 7.519

6.  An automatic behavior recognition system classifies animal behaviors using movements and their temporal context.

Authors:  Primoz Ravbar; Kristin Branson; Julie H Simpson
Journal:  J Neurosci Methods       Date:  2019-08-12       Impact factor: 2.390

7.  Digital video recorder for Raspberry PI cameras with multi-camera synchronous acquisition.

Authors:  Ghadi Salem; Jonathan Krynitsky; Noah Cubert; Alex Pu; Simeon Anfinrud; Jonathan Pedersen; Joshua Lehman; Ajith Kanuri; Thomas Pohida
Journal:  HardwareX       Date:  2020-11-26

8.  anTraX, a software package for high-throughput video tracking of color-tagged insects.

Authors:  Asaf Gal; Jonathan Saragosti; Daniel Jc Kronauer
Journal:  Elife       Date:  2020-11-19       Impact factor: 8.140

9.  TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields.

Authors:  Tristan Walter; Iain D Couzin
Journal:  Elife       Date:  2021-02-26       Impact factor: 8.140

10.  Improved 3D tracking and automated classification of rodents' behavioral activity using depth-sensing cameras.

Authors:  Ana Gerós; Ana Magalhães; Paulo Aguiar
Journal:  Behav Res Methods       Date:  2020-10
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