All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Project MultiLeap: Fusing Data from Multiple Leap Motion Sensors

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00356091" target="_blank" >RIV/68407700:21240/21:00356091 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/ICVR51878.2021.9483819" target="_blank" >https://doi.org/10.1109/ICVR51878.2021.9483819</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICVR51878.2021.9483819" target="_blank" >10.1109/ICVR51878.2021.9483819</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Project MultiLeap: Fusing Data from Multiple Leap Motion Sensors

  • Original language description

    Finding a simple and precise way to control the virtual environment is one of the goals of a lot of human-computer interaction research. One of the approaches is using a Leap Motion optical sensor, which provides hand and finger tracking without the need for any hand-held device. However, the Leap Motion system currently supports only one sensor at a time. To overcome this limitation, we proposed a set of algorithms to combine the data from multiple Leap Motion sensors to increase the precision and the usability of hand tracking. First, we suggested a way how to improve the calibration of the current hand pose alignment proposed by Leap Motion. Then, we proposed an approach to fuse the tracking data from multiple Leap Motion sensors to provide more precise interaction with the virtual world. For this, we implemented our very own algorithm for computing the confidence level of the tracking data that can be used to distinguish which Leap Motion sensor detects the tracked hands best. We implemented those algorithms into our MultiLeap library. We also created two demo scenes that we used to validate the correctness of our work - one for evaluation of the fusing algorithms and one for mimicking the interaction with control panels in a helicopter cockpit.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of 7th IEEE International Conference on Virtual Reality

  • ISBN

    9781665423090

  • ISSN

    2331-9569

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    19-25

  • Publisher name

    IEEE

  • Place of publication

    Beijing

  • Event location

    Foshan

  • Event date

    May 20, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article