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Markerless 3D Skeleton Tracking Algorithm by Merging Multiple Inaccurate Skeleton Data from Multiple RGB-D Sensors.

Sang-Hyub Lee¹, Deok-Won Lee¹, Kooksung Jun¹

¹School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju 61005, Korea.

Sensors (Basel, Switzerland)

|May 20, 2022

Summary

This summary is machine-generated.

This study presents a novel method to merge multiple noisy skeleton data sets into one accurate representation. The approach improves joint position accuracy, especially with more sensors, using DBSCAN and Kalman filters.

Keywords:

motion capture multiple RGB-D sensors sensor fusion skeleton tracking

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Area of Science:

Human-Computer Interaction (HCI)
Computer Vision
Robotics

Background:

Skeleton data, comprising 3D joint positions, is crucial for human pose and gesture recognition in HCI.
RGB-D sensors facilitate skeleton data capture, but single-sensor tracking suffers from occlusion, degrading joint data quality.
Reliable multi-angle tracking necessitates combining data from multiple sensors to overcome occlusion issues.

Purpose of the Study:

To develop a robust method for merging multiple inaccurate skeleton data sets into a single, accurate skeleton data.
To enhance the reliability and precision of human pose tracking by integrating data from diverse viewpoints.
To address the challenge of noise and inaccuracies inherent in skeleton data captured by individual sensors.

Main Methods:

Proposed a novel algorithm to combine multiple inaccurate skeleton data sets from different sensor angles.
Utilized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to filter out inaccurate joint candidates.
Employed a Kalman filter to denoise the temporal movement errors of the merged joint data.

Main Results:

The proposed algorithm successfully merged multiple skeleton data sets, improving overall joint position accuracy.
Performance evaluation indicated that accuracy increased with a higher number of sensors used for data capture.
Optimal performance was achieved when the DBSCAN searching area was set to 10 cm, demonstrating the algorithm's sensitivity to parameter tuning.

Conclusions:

The developed method effectively integrates multiple noisy skeleton data streams into a single, accurate representation.
Increasing the number of sensors significantly enhances the joint position accuracy of the merged skeleton data.
The algorithm provides a robust solution for accurate human pose tracking in scenarios with potential occlusions, with DBSCAN parameter tuning being critical for optimal results.