A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents

Walter Vanzella; Natalia Grion; Daniele Bertolini; Andrea Perissinotto; Marco Gigante; Davide Zoccolan

doi:10.1152/jn.00301.2019

journal article Dec 01, 2019

A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents

Walter Vanzella Natalia Grion Daniele Bertolini Andrea Perissinotto Marco Gigante Davide Zoccolan

Journal of Neurophysiology Vol. 122 No. 6 pp. 2220-2242 · American Physiological Society

View at Publisher Save 10.1152/jn.00301.2019

Abstract

Tracking head position and orientation in small mammals is crucial for many applications in the field of behavioral neurophysiology, from the study of spatial navigation to the investigation of active sensing and perceptual representations. Many approaches to head tracking exist, but most of them only estimate the 2D coordinates of the head over the plane where the animal navigates. Full reconstruction of the pose of the head in 3D is much more more challenging and has been achieved only in handful of studies, which employed headsets made of multiple LEDs or inertial units. However, these assemblies are rather bulky and need to be powered to operate, which prevents their application in wireless experiments and in the small enclosures often used in perceptual studies. Here we propose an alternative approach, based on passively imaging a lightweight, compact, 3D structure, painted with a pattern of black dots over a white background. By applying a cascade of feature extraction algorithms that progressively refine the detection of the dots and reconstruct their geometry, we developed a tracking method that is highly precise and accurate, as assessed through a battery of validation measurements. We show that this method can be used to study how a rat samples sensory stimuli during a perceptual discrimination task and how a hippocampal place cell represents head position over extremely small spatial scales. Given its minimal encumbrance and wireless nature, our method could be ideal for high-throughput applications, where tens of animals need to be simultaneously and continuously tracked.
NEW & NOTEWORTHY Head tracking is crucial in many behavioral neurophysiology studies. Yet reconstruction of the head’s pose in 3D is challenging and typically requires implanting bulky, electrically powered headsets that prevent wireless experiments and are hard to employ in operant boxes. Here we propose an alternative approach, based on passively imaging a compact, 3D dot pattern that, once implanted over the head of a rodent, allows estimating the pose of its head with high precision and accuracy.

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References

97

[1]

10.1016/j.cell.2015.12.015

[2]

10.1523/jneurosci.3629-12.2013

[3]

10.1038/s41467-017-01371-0

[4]

10.1016/j.jneumeth.2010.12.016

[5]

10.1038/nphys3035

[6]

10.1016/j.cub.2013.04.012

[7]

10.1523/jneurosci.21-15-05740.2001

[8]

10.1038/239500a0

[9]

10.1109/3dimpvt.2011.55

[10]

10.3389/fnbeh.2016.00235

[11]

10.1016/b978-0-08-051581-6.50024-6

[12]

10.1038/nphys4153

[13]

10.1523/jneurosci.2728-16.2017

[14]

10.1016/j.neuron.2009.07.027

[15]

10.1167/15.15.14

[16]

10.7554/elife.27702

[17]

10.1016/j.cub.2018.02.037

[18]

Duda RO (2001)

[19]

10.1038/nature14031

[20]

10.1109/34.908965

[21]

10.1126/science.1099901

[22]

10.1523/jneurosci.3808-04.2005

[23]

10.1109/3dimpvt.2012.40

[24]

González RC (2008)

[25]

10.1093/acprof:oso/9780198510581.001.0001

[26]

10.2200/s00332ed1v01y201103aim011

[27]

Guitchounts G (2019)

[28]

10.1038/nature03721

[29]

10.5244/c.2.23

[30]

10.1007/s00429-019-01854-4

[31]

10.1038/nprot.2013.122

[32]

10.1007/978-3-319-46466-4_3

[33]

Jähne B (2005)

[34]

10.1152/jn.00673.2017

[35]

10.1093/cercor/bhr233

[36]

10.3389/fnbeh.2012.00049

[37]

10.1152/jn.00718.2004

[38]

10.1038/nature14622

[39]

10.1016/j.cub.2017.01.013

[40]

10.1016/j.visres.2014.10.031

[41]

10.1016/j.bbr.2017.07.046

[42]

EPnP: An Accurate O(n) Solution to the PnP Problem

Vincent Lepetit, Francesc Moreno-Noguer, Pascal Fua

International Journal of Computer Vision 10.1007/s11263-008-0152-6

[43]

10.1523/jneurosci.0105-17.2017

[44]

Feature Detection with Automatic Scale Selection

Tony Lindeberg

International Journal of Computer Vision 10.1023/a:1008045108935

[45]

10.1109/34.134043

[46]

Distinctive Image Features from Scale-Invariant Keypoints

David G. Lowe

International Journal of Computer Vision 10.1023/b:visi.0000029664.99615.94

[47]

10.1038/nprot.2013.123

[48]

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Alexander Mathis, Pranav Mamidanna, Kevin M. Cury et al.

Nature Neuroscience 10.1038/s41593-018-0209-y

[49]

10.1523/jneurosci.1938-18.2018

[50]

10.1152/jn.00608.2004

Showing 50 of 97 references

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Details

Published: Dec 01, 2019
Vol/Issue: 122(6)
Pages: 2220-2242

Authors

W

Walter Vanzella

Visual Neuroscience Laboratory, International School for Advanced Studies (SISSA), Trieste, Italy; Glance Vision Technologies, Trieste, Italy

N

Natalia Grion

Visual Neuroscience Laboratory, International School for Advanced Studies (SISSA), Trieste, Italy

D

Daniele Bertolini

Visual Neuroscience Laboratory, International School for Advanced Studies (SISSA), Trieste, Italy

A

Andrea Perissinotto

Visual Neuroscience Laboratory, International School for Advanced Studies (SISSA), Trieste, Italy; Glance Vision Technologies, Trieste, Italy

M

Marco Gigante

Mechatronics Lab, International School for Advanced Studies (SISSA), Trieste, Italy

D

Davide Zoccolan

Visual Neuroscience Laboratory, International School for Advanced Studies (SISSA), Trieste, Italy

Funding

Human Frontier Science Program Award: RGP0015/2013

EC | European Research Council Award: 616803-LEARN2SEE

FSE POR Regione Autonoma FVG Program Grant Award: HEaD

Cite This Article

Walter Vanzella, Natalia Grion, Daniele Bertolini, et al. (2019). A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents. Journal of Neurophysiology, 122(6), 2220-2242. https://doi.org/10.1152/jn.00301.2019

A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents

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