Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

BEHAVIORAL SCIENCE

Behavioral tracking gets real

A deep-learning-based software package called DeepLabCut rapidly and easily enables video-based motion tracking in any animal species. Such tracking technology is bound to revolutionize movement science and behavioral tracking in the laboratory and is also poised to find many applications in the real world.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Motion capture examples.

References

  1. Stringer, C. et al. Preprint at bioRxiv https://doi.org/10.1101/306019 (2018).

    Article  Google Scholar 

  2. Muybridge, E. Animal Locomotion: An Electro-photographic Investigation of Consecutive Phases of Animal Movements 1872–1885 (Univ. of Pennsylvania, Philadelphia, 1887).

  3. Marey, E.-J. Le mouvement (G. Masson, Paris, 1894).

  4. Johansson, G. Sci. Am. 232, 76–88 (1975).

    CAS  Article  PubMed  Google Scholar 

  5. Mathis, A. et al. Nat. Neurosci. https://doi.org/10.1038/s41593-018-0209-y (2018).

    Article  PubMed  Google Scholar 

  6. Aggarwal, J. K. & Duda, R. O. IEEE Trans. Comput. C-24, 966–976 (1975).

    Article  Google Scholar 

  7. Dell, A. I. et al. Trends Ecol. Evol. 29, 417–428 (2014).

    Article  PubMed  Google Scholar 

  8. Guo, J. Z. et al. Elife 4, e10774 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Deng, J. et al. ImageNet: a large-scale hierarchical image database. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2009 248–255 (IEEE, Piscataway, NJ, USA, 2009).

  10. Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M. & Schiele, B. DeeperCut: a deeper, stronger, and faster multi-person pose estimation model. in European Conference on Computer Vision 34–50 (Springer, New York, 2016).

  11. Barris, S. & Button, C. Sports Med. 38, 1025–1043 (2008).

    Article  PubMed  Google Scholar 

  12. Mayhew, S. & Wenger, H. J. Hum. Mov. Stud. 11, 49–52 (1985).

    Google Scholar 

  13. Jones, E. J., Gliga, T., Bedford, R., Charman, T. & Johnson, M. H. Neurosci. Biobehav. Rev. 39, 1–33 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhou, H. & Hu, H. Biomed. Signal Process. Control 3, 1–18 (2008).

    CAS  Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Kunlin Wei or Konrad Paul Kording.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wei, K., Kording, K.P. Behavioral tracking gets real. Nat Neurosci 21, 1146–1147 (2018). https://doi.org/10.1038/s41593-018-0215-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-018-0215-0

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing