Measurement Noise Model for Depth Camera-Based People Tracking
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 model...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/13/4488 |
| _version_ | 1797528189353852928 |
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| author | Otto Korkalo Tapio Takala |
| author_facet | Otto Korkalo Tapio Takala |
| author_sort | Otto Korkalo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:54:38Z |
| format | Article |
| id | doaj.art-48033c2af391492b825d6f951678e4ee |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:54:38Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-48033c2af391492b825d6f951678e4ee2023-11-22T02:26:23ZengMDPI AGSensors1424-82202021-06-012113448810.3390/s21134488Measurement Noise Model for Depth Camera-Based People TrackingOtto Korkalo0Tapio Takala1VTT Technical Research Centre of Finland Ltd., P.O. Box 1000, FI-02044 Espoo, FinlandDepartment of Computer Science, Aalto University, FI-00076 Espoo, FinlandDepth 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.https://www.mdpi.com/1424-8220/21/13/4488people trackingdepth camerasmeasurement noise modelsdata fusionmultiple-view tracking |
| spellingShingle | Otto Korkalo Tapio Takala Measurement Noise Model for Depth Camera-Based People Tracking Sensors people tracking depth cameras measurement noise models data fusion multiple-view tracking |
| title | Measurement Noise Model for Depth Camera-Based People Tracking |
| title_full | Measurement Noise Model for Depth Camera-Based People Tracking |
| title_fullStr | Measurement Noise Model for Depth Camera-Based People Tracking |
| title_full_unstemmed | Measurement Noise Model for Depth Camera-Based People Tracking |
| title_short | Measurement Noise Model for Depth Camera-Based People Tracking |
| title_sort | measurement noise model for depth camera based people tracking |
| topic | people tracking depth cameras measurement noise models data fusion multiple-view tracking |
| url | https://www.mdpi.com/1424-8220/21/13/4488 |
| work_keys_str_mv | AT ottokorkalo measurementnoisemodelfordepthcamerabasedpeopletracking AT tapiotakala measurementnoisemodelfordepthcamerabasedpeopletracking |