Real-time hand tracking using a mean shift embedded particle filter

  • Authors:

  • Caifeng Shan;Tieniu Tan;Yucheng Wei

  • Affiliations:

  • Department of Computer Science, Queen Mary University of London, Mile End Road, London E1 4NS, UK;National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing 100080, China;National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing 100080, China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2007

Quantified Score

Hi-index 0.01

Visualization

Abstract

Particle filtering and mean shift (MS) are two successful approaches to visual tracking. Both have their respective strengths and weaknesses. In this paper, we propose to integrate advantages of the two approaches for improved tracking. By incorporating the MS optimization into particle filtering to move particles to local peaks in the likelihood, the proposed mean shift embedded particle filter (MSEPF) improves the sampling efficiency considerably. Our work is conducted in the context of developing a hand control interface for a robotic wheelchair. We realize real-time hand tracking in dynamic environments of the wheelchair using MSEPF. Extensive experimental results demonstrate that MSEPF outperforms the MS tracker and the conventional particle filter in hand tracking. Our approach produces reliable tracking while effectively handling rapid motion and distraction with roughly 85% fewer particles. We also present a simple method for dynamic gesture recognition. The hand control interface based on the proposed algorithms works well in dynamic environments of the wheelchair.