Auto-calibration of depth camera networks for people tracking

Otto Korkalo (Corresponding Author), Tommi Tikkanen, Paul Kemppi, Petri Honkamaa

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

We address the problem of calibrating an embedded depth camera network designed for people tracking purposes. In our system, the nodes of the network are responsible for detecting the people moving in their view, and sending the observations to a centralized server for data fusion and tracking. We employ a plan-view approach where the depth camera views are transformed to top-view height maps where people are observed. As the server transforms the observations to a global plan-view coordinate system, accurate geometric calibration of the sensors has to be performed. Our main contribution is an auto-calibration method for such depth camera networks. In our approach, the sensor network topology and the initial 2D rigid transformations that map the observations to the global frame are determined using observations only. To distribute the errors in the initial calibration, the transformation parameters and the estimated positions of people are refined using a global optimization routine. To overcome inaccurate depth camera parameters, we re-calibrate the sensors using more flexible transformations, and experiment with similarity, affine, homography and thin-plate spline mappings. We evaluate the robustness, accuracy and precision of the approach using several real-life data sets, and compare the results to a checkerboard-based calibration method as well as to the ground truth trajectories collected with a mobile robot.

Original languageEnglish
Number of pages18
JournalMachine Vision and Applications
DOIs
Publication statusAccepted/In press - 28 Mar 2019
MoE publication typeA1 Journal article-refereed

Fingerprint

Cameras
Calibration
Servers
Sensors
Data fusion
Global optimization
Splines
Mobile robots
Sensor networks
Trajectories
Topology
Experiments

Keywords

  • Auto-calibration
  • Depth camera network
  • Depth distortion
  • People tracking
  • Thin-plate spline

Cite this

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title = "Auto-calibration of depth camera networks for people tracking",
abstract = "We address the problem of calibrating an embedded depth camera network designed for people tracking purposes. In our system, the nodes of the network are responsible for detecting the people moving in their view, and sending the observations to a centralized server for data fusion and tracking. We employ a plan-view approach where the depth camera views are transformed to top-view height maps where people are observed. As the server transforms the observations to a global plan-view coordinate system, accurate geometric calibration of the sensors has to be performed. Our main contribution is an auto-calibration method for such depth camera networks. In our approach, the sensor network topology and the initial 2D rigid transformations that map the observations to the global frame are determined using observations only. To distribute the errors in the initial calibration, the transformation parameters and the estimated positions of people are refined using a global optimization routine. To overcome inaccurate depth camera parameters, we re-calibrate the sensors using more flexible transformations, and experiment with similarity, affine, homography and thin-plate spline mappings. We evaluate the robustness, accuracy and precision of the approach using several real-life data sets, and compare the results to a checkerboard-based calibration method as well as to the ground truth trajectories collected with a mobile robot.",
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Auto-calibration of depth camera networks for people tracking. / Korkalo, Otto (Corresponding Author); Tikkanen, Tommi; Kemppi, Paul; Honkamaa, Petri.

In: Machine Vision and Applications, 28.03.2019.

Research output: Contribution to journalArticleScientificpeer-review

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