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BEHAVIORAL SCIENCE

Behavioral tracking gets real

Nature Neuroscience volume 21, pages 1146–1147 (2018)Cite this article

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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.

The limits of motion tracking technologies are felt all over the field. For instance, hand reaching, which is easily tracked, is the most popular model in human motor control studies. However, focusing on hand movements, often confined in the horizontal plane by a fixed shoulder, obscures the real beauty of human movement control: we walk, run, jump, dance, and interact with other people and animals, both indoors and outdoors. All these rich behaviors are largely left unexplored and potentially misunderstood by extrapolating from findings on small hand movements. Similarly, we know relatively little about the movement of rodents, in large part because no good standard movement tracking systems exist. Real-world motion tracking should also be easy to use, free of interference with behavior, and based solely on video recordings. DeepLabCut answers all those needs. This may sound very much like magic, so let us go through what has made DeepLabCut such an accurate and easy-to-use tool for motion tracking.

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Fig. 1: Motion capture examples.

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Authors and Affiliations

  1. University of Peking, Psychology, Beijing, China

    Kunlin Wei

  2. University of Pennsylvania, Bioengineering and Neuroscience, Philadelphia, PA, USA

    Konrad Paul Kording

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  1. Kunlin Wei
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  2. Konrad Paul Kording
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Correspondence to Kunlin Wei or Konrad Paul Kording.

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

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