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

 

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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.

 

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