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


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).

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Figure 1: idTracker maintains correct identities without propagation of errors.
Figure 2: Identification method.
Figure 3: Examples of applications of idTracker.
Figure 4: Analysis of social behavior using idTracker.


  1. Ohayon, S., Avni, O., Taylor, A.L., Perona, P. & Egnor, S.E.R. Automated multi-day tracking of marked mice for the analysis of social behavior. J. Neurosci. Methods 219, 10–19 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  3. Dennis, R.L., Newberry, R.C., Cheng, H.-W. & Estevez, I. Appearance matters: artificial marking alters aggression and stress. Poult. Sci. 87, 1939–1946 (2008).

    Article  CAS  PubMed  Google Scholar 

  4. Dahlbom, S.J., Lagman, D., Lundstedt-Enkel, K., Sundström, L.F. & Winberg, S. Boldness predicts social status in zebrafish (Danio rerio). PLoS ONE 6, e23565 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Delcourt, J., Becco, C., Vandewalle, N. & Poncin, P. A video multitracking system for quantification of individual behavior in a large fish shoal: advantages and limits. Behav. Res. Methods 41, 228–235 (2009).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  7. Mirat, O., Sternberg, J.R., Severi, K.E. & Wyart, C. ZebraZoom: an automated program for high-throughput behavioral analysis and categorization. Front. Neural Circuits 7, 107 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Branson, K., Robie, A.A., Bender, J., Perona, P. & Dickinson, M.H. High-throughput ethomics in large groups of Drosophila. Nat. Methods 6, 451–457 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Branson, K. & Belongie, S. in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 1039–1046 (IEEE, 2005).

    Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

  14. Straw, A.D., Branson, K., Neumann, T.R. & Dickinson, M.H. Multi-camera real-time three-dimensional tracking of multiple flying animals. J. R. Soc. Interface 8, 395–409 (2011).

    Article  PubMed  Google Scholar 

  15. Herbert-Read, J.E. et al. Inferring the rules of interaction of shoaling fish. Proc. Natl. Acad. Sci. USA 108, 18726–18731 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Huang, J., Kumar, S.R., Mitra, M., Zhu, W.-J. & Zabih, R. Spatial color indexing and applications. Int. J. Comput. Vis. 35, 245–268 (1999).

    Article  Google Scholar 

  17. Lister, J.A., Robertson, C.P., Lepage, T., Johnson, S.L. & Raible, D.W. nacre encodes a zebrafish microphtalmia-related protein that regulates neural-crest-derived pigment cell fate. Development 126, 3757–3767 (1999).

    CAS  PubMed  Google Scholar 

  18. Sumpter, D.J.T. Collective Animal Behavior (Princeton University Press, 2010).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Vicsek, T. & Zafeiris, A. Collective motion. Phys. Rep. 517, 71–140 (2012).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  24. Arganda, S., Pérez-Escudero, A. & de Polavieja, G.G. A common rule for decision-making in animal collectives across species. Proc. Natl. Acad. Sci. USA 109, 20508–20513 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Nagy, M., Akos, Z., Biro, D. & Vicsek, T. Hierarchical group dynamics in pigeon flocks. Nature 464, 890–893 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    Google Scholar 

  28. Turk, M.A. & Pentland, A.P. in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 586–591 (1991).

  29. Khotanzad, A. & Hong, Y.H. Rotation invariant image recognition using features selected via a systematic method. Pattern Recognit. 23, 1089–1101 (1990).

    Article  Google Scholar 

  30. Wu, X., Zhang, D. & Wang, K. Fisherpalms based palmprint recognition. Pattern Recognit. Lett. 24, 2829–2838 (2003).

    Article  Google Scholar 

  31. Crall, J.P., Stewart, C.V., Berger-Wolf, T.Y., Rubenstein, D.I. & Sundaresan, S.R. in Proc. IEEE Workshop Appl. Comput. Vis. 230–237 (2013).

  32. Bhatkar, A. & Whitcomb, W.H. Artificial diet for rearing various species of ants. Fla. Entomol. 53, 229–232 (1970).

    Article  Google Scholar 

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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

Authors and Affiliations



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.

Corresponding authors

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

Ethics declarations

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

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Table 1 and Supplementary Notes 1–4 (PDF 16844 kb)

Supplementary Software

Source code of idTracker (Matlab and C) (ZIP 130 kb)

Supplementary Data

Raw data and scripts to reproduce the results in Figure 4 (ZIP 45931 kb)

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. (MP4 20213 kb)

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Pérez-Escudero, A., Vicente-Page, J., Hinz, R. et al. idTracker: tracking individuals in a group by automatic identification of unmarked animals. Nat Methods 11, 743–748 (2014).

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