Measurement noise model for depth camera-based people tracking
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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2021-07-01
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en
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20
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Sensors, Volume 21, issue 13
Abstract
Depth cameras are widely used in people tracking applications. They typically suffer from significant range measurement noise, which causes uncertainty in the detections made of the people. The data fusion, state estimation and data association tasks require that the measurement uncertainty is modelled, especially in multi-sensor systems. Measurement noise models for different kinds of depth sensors have been proposed, however, the existing approaches require manual calibration procedures which can be impractical to conduct in real-life scenarios. In this paper, we present a new measurement noise model for depth camera-based people tracking. In our tracking solution, we utilise the so-called plan-view approach, where the 3D measurements are transformed to the floor plane, and the tracking problem is solved in 2D. We directly model the measurement noise in the plan-view domain, and the errors that originate from the imaging process and the geometric transformations of the 3D data are combined. We also present a method for directly defining the noise models from the observations. Together with our depth sensor network self-calibration routine, the approach allows fast and practical deployment of depth-based people tracking systems.Description
Funding Information: VTT self-funded. Acknowledgments: The authors would like to thank Paul Kemppi at VTT Technical Research Centre of Finland for their help with the mobile robot, and Petri Honkamaa at VTT Technical Research Centre of Finland for the fruitful discussions and their help in implementation of the autocalibration method. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
Data fusion, Depth cameras, Measurement noise models, Multiple-view tracking, People tracking
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Citation
Korkalo, O & Takala, T 2021, ' Measurement noise model for depth camera-based people tracking ', Sensors, vol. 21, no. 13, 4488 . https://doi.org/10.3390/s21134488