Article | Published:

idTracker: tracking individuals in a group by automatic identification of unmarked animals

Nature Methods volume 11, pages 743748 (2014) | Download Citation

Abstract

Animals in groups touch each other, move in paths that cross, and interact in complex ways. Current video tracking methods sometimes switch identities of unmarked individuals during these interactions. These errors propagate and result in random assignments after a few minutes unless manually corrected. We present idTracker, a multitracking algorithm that extracts a characteristic fingerprint from each animal in a video recording of a group. It then uses these fingerprints to identify every individual throughout the video. Tracking by identification prevents propagation of errors, and the correct identities can be maintained indefinitely. idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. We tested it on fish (Danio rerio and Oryzias latipes), flies (Drosophila melanogaster), ants (Messor structor) and mice (Mus musculus).

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    , , , & Automated multi-day tracking of marked mice for the analysis of social behavior. J. Neurosci. Methods 219, 10–19 (2013).

  2. 2.

    et al. High-order social interactions in groups of mice. eLife 2, e00759 (2013).

  3. 3.

    , , & Appearance matters: artificial marking alters aggression and stress. Poult. Sci. 87, 1939–1946 (2008).

  4. 4.

    , , , & Boldness predicts social status in zebrafish (Danio rerio). PLoS ONE 6, e23565 (2011).

  5. 5.

    , , & A video multitracking system for quantification of individual behavior in a large fish shoal: advantages and limits. Behav. Res. Methods 41, 228–235 (2009).

  6. 6.

    et al. A computer image processing system for quantification of zebrafish behavior. J. Neurosci. Methods 134, 1–7 (2004).

  7. 7.

    , , & ZebraZoom: an automated program for high-throughput behavioral analysis and categorization. Front. Neural Circuits 7, 107 (2013).

  8. 8.

    , , , & High-throughput ethomics in large groups of Drosophila. Nat. Methods 6, 451–457 (2009).

  9. 9.

    & in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 1039–1046 (IEEE, 2005).

  10. 10.

    et al. Automated visual tracking for studying the ontogeny of zebrafish swimming. J. Exp. Biol. 211, 1305–1316 (2008).

  11. 11.

    et al. Computerized video analysis of social interactions in mice. Nat. Methods 9, 410–417 (2012).

  12. 12.

    & Three-dimensional reconstruction of the fast-start swimming kinematics of densely schooling fish. J. R. Soc. Interface 9, 77–88 (2012).

  13. 13.

    et al. Tracking in three dimensions via multi-path branching. Preprint (2013).

  14. 14.

    , , & Multi-camera real-time three-dimensional tracking of multiple flying animals. J. R. Soc. Interface 8, 395–409 (2011).

  15. 15.

    et al. Inferring the rules of interaction of shoaling fish. Proc. Natl. Acad. Sci. USA 108, 18726–18731 (2011).

  16. 16.

    , , , & Spatial color indexing and applications. Int. J. Comput. Vis. 35, 245–268 (1999).

  17. 17.

    , , , & nacre encodes a zebrafish microphtalmia-related protein that regulates neural-crest-derived pigment cell fate. Development 126, 3757–3767 (1999).

  18. 18.

    Collective Animal Behavior (Princeton University Press, 2010).

  19. 19.

    et al. Scale-free correlations in starting flocks. Proc. Natl. Acad. Sci. USA 107, 11865–11870 (2010).

  20. 20.

    et al. Visual attention and the acquisition of information in human crowds. Proc. Natl. Acad. Sci. USA 109, 7245–7250 (2012).

  21. 21.

    et al. Deciphering interactions in moving animal groups. PLoS Comput. Biol. 8, e1002678 (2012).

  22. 22.

    & Collective motion. Phys. Rep. 517, 71–140 (2012).

  23. 23.

    et al. Selfish-herd behavior of sheep under threat. Curr. Biol. 22, R561–R562 (2012).

  24. 24.

    , & A common rule for decision-making in animal collectives across species. Proc. Natl. Acad. Sci. USA 109, 20508–20513 (2012).

  25. 25.

    et al. Visual sensory networks and effective information transfer in animal groups. Curr. Biol. 23, R709–R711 (2013).

  26. 26.

    , , & Hierarchical group dynamics in pigeon flocks. Nature 464, 890–893 (2010).

  27. 27.

    in Proc. IEEE Int. Conf. Comput. Vis. 2, 1150–1157 (1999).

  28. 28.

    & in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 586–591 (1991).

  29. 29.

    & Rotation invariant image recognition using features selected via a systematic method. Pattern Recognit. 23, 1089–1101 (1990).

  30. 30.

    , & Fisherpalms based palmprint recognition. Pattern Recognit. Lett. 24, 2829–2838 (2003).

  31. 31.

    , , , & in Proc. IEEE Workshop Appl. Comput. Vis. 230–237 (2013).

  32. 32.

    & Artificial diet for rearing various species of ants. Fla. Entomol. 53, 229–232 (1970).

Download references

Acknowledgements

We thank P. Bovolenta (Centro de Biología Molecular Severo Ochoa, Consejo Superior de Investigaciones Científicas (CSIC)), A. Dussutour (Centre de Recherches sur la Cognition Animale, Centre National de la Recherche Scientifique, Toulouse University), A. Ferrús (Instituto Cajal, CSIC), A. Sorribes (Instituto Cajal, CSIC), G. Sumbre (Ecole Normale Supérieure) and J.L. Trejo (Instituto Cajal, CSIC) for providing animals. We also thank I. Arganda-Carreras, K. Branson, I. Couzin, J. Gautrais, R. Ottenhoff, H. Roosken, R. Tegelenbosch, W. van Dommelen and H. Wu for feedback; A. Bruce, A. King, E. Pope, G. Thelauraz, R. Goulard, U. Lopez and D. Calovi for testing in their labs; and R. Gil de Sagredo, G. Madirolas, A.C. Román and F. Romero-Ferrero for a critical reading of the paper. We acknowledge an FPU fellowship from Ministerio de Economía y Competitividad, Spain (to A.P.-E.), an FPI fellowship from Ministerio de Economía y Competitividad (to R.C.H.), a JAE fellowship from CSIC (to J.V.-P.), funding from Spanish Plan Nacional BFU2009-09967 and BFU2012-33448 from Ministerio de Economía y Competitividad (to G.G.d.P.) and funding from ERASysBio+ initiative supported under the European Union European Research Area Networks (ERA-NET) Plus scheme in the FP7 Seventh Framework Programme as EUI2009-04090 (to G.G.d.P., including contracts to S.A. and A.P.-E.).

Author information

Author notes

    • Sara Arganda

    Present address: Centre de Recherches sur la Cognition Animale, Centre National de la Recherche Scientifique, Université Paul Sabatier, Toulouse, France.

Affiliations

  1. Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain.

    • Alfonso Pérez-Escudero
    • , Julián Vicente-Page
    • , Robert C Hinz
    • , Sara Arganda
    •  & Gonzalo G de Polavieja

Authors

  1. Search for Alfonso Pérez-Escudero in:

  2. Search for Julián Vicente-Page in:

  3. Search for Robert C Hinz in:

  4. Search for Sara Arganda in:

  5. Search for Gonzalo G de Polavieja in:

Contributions

A.P.-E. designed the project, performed experiments, analyzed data, wrote and tested the software and wrote the paper; R.C.H. performed experiments, tested the software and analyzed data; J.V.-P. performed experiments, tested the software and analyzed data; S.A. performed experiments and tested the software, G.G.d.P. designed the project, advised and directed the project including experiments, software and data analysis and wrote the paper.

Competing interests

Consejo Superior de Investigaciones Cientificas (CSIC) owns a patent describing this method (patent no. PCT/ES2013/070585). A.P.-E., G.G.d.P. and S.A. are listed as inventors of the patent and will receive a part of the revenues that it may generate. Noncommercial use of the method and the associated software is allowed free of charge

Corresponding authors

Correspondence to Alfonso Pérez-Escudero or Gonzalo G de Polavieja.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–7, Supplementary Table 1 and Supplementary Notes 1–4

Zip files

  1. 1.

    Supplementary Software

    Source code of idTracker (Matlab and C)

  2. 2.

    Supplementary Data

    Raw data and scripts to reproduce the results in Figure 4

Videos

  1. 1.

    idTracker: Visual Summary

    This movie explains the main features and advantages of idTracker, outlines the method, and shows examples of multitracking of several species: zebrafish (Danio rerio), medaka fish (Oryzias latipes), fruitflies (Drosophila melanogaster), mice (Mus musculus) and ants.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmeth.2994

Further reading