Development of a Self-Calibrated Motion Capture System by Nonlinear Trilateration of Multiple Kinects v2

TL;DR

This paper presents a real-time, Kinect v2-based motion capture system that uses nonlinear trilateration and computational geometry to improve accuracy, handle occlusions, and synchronize data for practical applications.

Contribution

It introduces a novel nonlinear trilateration method combined with occlusion handling and dynamic synchronization for enhanced Kinect-based motion capture.

Findings

Accuracy improved by 38.3% over linear trilateration

System operates in real-time with dynamic synchronization

Outperforms benchmark systems in calibration and occlusion handling

Abstract

In this paper, a Kinect-based distributed and real-time motion capture system is developed. A trigonometric method is applied to calculate the relative position of Kinect v2 sensors with a calibration wand and register the sensors' positions automatically. By combining results from multiple sensors with a nonlinear least square method, the accuracy of the motion capture is optimized. Moreover, to exclude inaccurate results from sensors, a computational geometry is applied in the occlusion approach, which discovers occluded joint data. The synchronization approach is based on an NTP protocol that synchronizes the time between the clocks of a server and clients dynamically, ensuring that the proposed system is a real-time system. Experiments for validating the proposed system are conducted from the perspective of calibration, occlusion, accuracy, and efficiency. Furthermore, to demonstrate the practical performance of our system, a comparison of previously developed motion capture systems (the linear trilateration approach and the geometric trilateration approach) with the benchmark OptiTrack system is conducted, therein showing that the accuracy of our proposed system is $38.3\%$ and 24.1% better than the two aforementioned trilateration systems, respectively.