Influence of a marker-based motion capture system on the performance of Microsoft kinect v2 skeleton algorithm
(Einfluss eines markerbasierten Bewegungserfassungssystems auf die Leistung des Microsoft Kinect v2 Skeleton Algorithmus)
Microsoft Kinect sensors are being widely used as low-cost marker-less motion capture systems in various kinematic studies. Previous studies investigated the reliability and validity of Microsoft Kinect sensors by employing marker-based motion capture systems. Both systems employ infrared emitters and detectors to track human posture and physical activities. This paper hypothesizes that the motion capture systems may interfere with Microsoft Kinect one sensor and may influence the sensor's performance in tracking the skeleton. Hence, this paper investigated the impact of a motion capture system on the Microsoft Kinect v2 skeleton algorithm using a mannequin in the presence of eight Qualisys Oqus 300/310 cameras and retroreflective markers. It was found that the motion capture system introduced a destructive impact on the Microsoft Kinect v2 skeleton tracking algorithm. In addition, it was observed that retroreflective markers placed near the joints caused the Microsoft Kinect v2 to give an incorrect reading of estimate the joint position. The motion capture cameras thus caused a time-varying distortion of the Microsoft Kinect estimate of the joint position. It is believed that the inference can be reduced by decreasing the number of markers and avoiding facing the motion capture cameras in sight of Microsoft Kinect v2.
© Copyright 2019 IEEE Sensors Journal. IEEE Institute of Electrical and Electronics Engineers. Alle Rechte vorbehalten.
| Schlagworte: | |
|---|---|
| Notationen: | Naturwissenschaften und Technik |
| Tagging: | Kinect Marker Algorithmus |
| Veröffentlicht in: | IEEE Sensors Journal |
| Sprache: | Englisch |
| Veröffentlicht: |
2019
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| Online-Zugang: | https://doi.org/10.1109/JSEN.2018.2876624 |
| Jahrgang: | 19 |
| Heft: | 1 |
| Seiten: | 171-179 |
| Dokumentenarten: | Forschungsergebnis |
| Level: | hoch |