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Measurement Noise Model for Depth Camera-Based People Tracking.

Otto Korkalo1, Tapio Takala2

  • 1VTT Technical Research Centre of Finland Ltd., P.O. Box 1000, FI-02044 Espoo, Finland.

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|July 2, 2021
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Summary

This study introduces a new noise model for depth camera people tracking. It simplifies deployment by directly modeling noise in the 2D plan-view and learning from observations, removing manual calibration needs.

Keywords:
data fusiondepth camerasmeasurement noise modelsmultiple-view trackingpeople tracking

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • Depth cameras are crucial for people tracking but suffer from range measurement noise, impacting detection accuracy.
  • Accurate modeling of measurement uncertainty is essential for data fusion and state estimation, particularly in multi-sensor systems.
  • Existing noise models for depth sensors often require impractical manual calibration.

Purpose of the Study:

  • To develop a novel, practical measurement noise model for depth camera-based people tracking.
  • To enable automated noise model definition directly from observational data.
  • To facilitate easier and faster deployment of depth-based tracking systems.

Main Methods:

  • Utilized a plan-view approach, transforming 3D depth data to a 2D floor plane for tracking.
  • Developed a direct measurement noise model within the 2D plan-view domain, integrating imaging and geometric transformation errors.
  • Introduced a method for deriving noise models from sensor observations and integrated it with a self-calibration routine.

Main Results:

  • Successfully modeled measurement noise directly in the 2D plan-view domain.
  • Demonstrated a method for defining noise models from observed data, eliminating manual calibration.
  • The combined approach enables practical and efficient deployment of depth-based people tracking systems.

Conclusions:

  • The proposed noise model and automated definition method significantly improve the practicality of depth camera people tracking.
  • This work reduces the barrier to entry for deploying robust multi-sensor tracking systems using depth cameras.
  • The approach facilitates faster and more reliable real-world applications of people tracking technology.