Enlarging the Usable Hand Tracking Area by Using Multiple Leap Motion Controllers in VR

PubDate: May 2021

Teams: Beijing Insitute of Technology；University of Canterbur

Writers: Yu Wang; Yuanjie Wu; Sungchul Jung; Simon Hoermann; Shouwen Yao; Robert Lindeman

PDF: Enlarging the Usable Hand Tracking Area by Using Multiple Leap Motion Controllers in VR

Abstract

Leap Motion Controller (LMC) is a widely-used 3D user-interface device for virtual reality (VR) in hand tracking applications. However, the tracking area of a single LMC is not sufficient to cover the complete range of hand motion typically used in virtual reality applications, which can cause inconvenience and unnatural behavior of bare-hand interaction in a cooperated virtual environment. In this paper, we propose fusing the data from multiple LMCs to enlarge the tracking area. We present our shared-view calibration method based on a Least-squares Fitting algorithm. To track two hands in the enlarged tracking area, we propose a multi-targets tracking algorithm based on a Clustering-based Labeled Probability Hypothesis Density filter implemented by Gaussian mixture approach. A hand-recognition confidence is proposed to improve the tracking performance when hands are incorrectly recognized. The performance of the proposed algorithm was evaluated by three tests based on a five-LMCs system used on an Oculus Rift S. Results show that our system can track two hands stably in the range of 202.16 degrees horizontally and 164.43 degrees vertically, and the proposed algorithm shows superiority in tracking robustness under hand-recognition errors. The contribution of this paper is to provide a detailed guide for designing an enlarged hand-tracking system using sensor fusion.