3D hand tracking using Kalman filter in depth space

Sangheon Park, Sunjin Yu, Joongrock Kim, Sungjin Kim, Sangyoun Lee

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

Hand gestures are an important type of natural language used in many research areas such as human-computer interaction and computer vision. Hand gestures recognition requires the prior determination of the hand position through detection and tracking. One of the most efficient strategies for hand tracking is to use 2D visua information such as color and shape. However, visual-sensor-based hand tracking methods are very sensitive when tracking is performed under variable light conditions. Also, as hand movements are made in 3D space, the recognition performance of hand gestures using 2D information is inherently limited. In this article, we propose a novel real-time 3D hand tracking method in depth space using a 3D depth sensor and employing Kalman filter. We detect hand candidates using motion clusters and predefined wave motion, and track hand locations using Kalman filter. To verify the effectiveness of the proposed method, we compare the performance of the proposed method with the visual-based method. Experimental results show that the performance of the proposed method out performs visual-based method.

Original languageEnglish
Article number36
JournalEurasip Journal on Advances in Signal Processing
Volume2012
Issue number1
DOIs
Publication statusPublished - 2012 Dec 1

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All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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