bullet Sensors & Transducers Journal

    (ISSN: 2306-8515, e-ISSN 1726-5479)


2013 Global Impact Factor


2008 e-Impact Factor

25 Top Downloaded Articles

Best Selling Articles 2012

Journal Subscription 2014

Editorial Calendar

Submit an Article

Editorial Board

Current Issue

S&T journal's cover

Sensors & Transducers Journal 2011

Sensors & Transducers Journal 2010

Sensors & Transducers Journal 2009

Sensors & Transducers Journal 2008

Sensors & Transducers Journal 2007

2000-2002 S&T e-Digest Contents

2003 S&T e-Digest Contents

2004 S&T e-Digest Contents

2005 S&T e-Digest Contents

2006 S&T e-Digest Contents


Best Articles 2011




Vol. 177, Issue 8, August 2014, pp. 307-312




An Improved Camshift-based Particle Filter Algorithm for Real-time Hand Gesture Tracking
1 Zhang Fang Hu, 2 Yun Kai Wang, 1 Yuan Luo, 2 Yi Zhang, 1 Bing Xi

1 Key Laboratory of Optical Fiber Communication Technology Chongqing Education Commission,

Chongqing University of Post and Telecommunications, Chongqing 400065, P. R. China

2 Research Center of Intelligent System and Robot, Chongqing University of Post and

Telecommunications, Chongqing 400065, P. R. China

1 Tel.: +86-18725875566

1 E-mail: huzf@cqupt.edu.cn


Received: 24 May 2014   /Accepted: 31 July 2014   /Published: 31 August 2014

Digital Sensors and Sensor Sysstems


Abstract: In the study of dynamic gesture recognition, gesture tracking must be performed reliably in real-time for sufficiently long periods when there is too much background interference. To deal with these problems effectively, an improved particle filter algorithm is proposed in order to track the moving hand quickly and accurately. Firstly, the algorithm improves the traditional hand model and presents a novel hand model, which fuses color and depth cues, to enhance the robustness and accuracy of gesture tracking. Meanwhile, in order to increase the tracking efficiency, the Camshift algorithm is embedded into the particle filter to rearrange the random particles, in which the particles moved toward the maximal posterior probability density of the target state. Experimental results show that compared with the traditional particle filter algorithm or Camshift algorithm, the proposed method achieve fast and robust tracking of the hand with the situations of fast moving hand and strong disturbances in the background.


Keywords: Particles filter, Camshift, Color and depth model, Gesture tracking, Posterior probability.


Acrobat reader logo Click <here> or title of paper to download the full pages article in pdf format



Subscribe the full-page Sensors & Transducers journal in print (paper) or pdf formats

(shipping cost by standard mail for paper version is included)

(25 % discount for IFSA Members)




Alternatively we accept a money transfer to our bank account. Please contact for details: sales@sensorsportal.com



Download <here> the Library Journal Recommendation Form






1999 - 2014 Copyright , International Frequency Sensor Association (IFSA) Publishing, S.L. All Rights Reserved.

Home - News - Links - Archives - Tools - Voltage-to-Frequency Converters - Standardization - Patents - Marketplace - Projects - Wish List - e-Shop - Sensor Jobs - Membership - Videos - Publishing - Site Map - Subscribe - Search

 Members Area -Sensors Portal -Training Courses - S&T Digest - For advertisers - Bookstore - Forums - Polls - Submit Press Release - Submit White Paper - Testimonies - Twitter - Facebook - LinkedIn