New color-based tracking algorithm for joints of the upper extremities

Xiangping WU, Hung Kay Daniel CHOW, Xiaoxiang ZHENG

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

To track the joints of the upper limb of stroke sufferers for rehabilitation assessment, a new tracking algorithm which utilizes a developed color-based particle filter and a novel strategy for handling occlusions is proposed in this paper. Objects are represented by their color histogram models and particle filter is introduced to track the objects within a probability framework. Kalman filter, as a local optimizer, is integrated into the sampling stage of the particle filter that steers samples to a region with high likelihood and therefore fewer samples is required. A color clustering method and anatomic constraints are used in dealing with occlusion problem. Compared with the general basic particle filtering method, the experimental results show that the new algorithm has reduced the number of samples and hence the computational consumption, and has achieved better abilities of handling complete occlusion over a few frames. Copyright © 2007 Society of Photo-Optical Instrumentation Engineers (SPIE).

Original languageEnglish
Title of host publicationMIPPR 2007: Automatic target recognition and image analysis; and multispectral image acquisition
EditorsTianxu ZHANG, Carl Anthony NARDELL, Duane D. SMITH, Hangqing LU, Tianxu ZHANG, Carl Anthony NARDELL, Hanqing LU
Place of PublicationBellingham
PublisherSPIE Press
ISBN (Print)9780819469502, 0819469505
DOIs
Publication statusPublished - 01 Dec 2007

Fingerprint

Color
Kalman filters
Patient rehabilitation
Sampling

Citation

Wu, X., Chow, D. H. K., & Zheng, X. (2007). New color-based tracking algorithm for joints of the upper extremities. In T. Zhang, C. A. Nardell, D. D. Smith, H. Lu, T. Zhang, C. A. Nardell, & H. Lu (Eds.), MIPPR 2007: Automatic target recognition and image analysis; and multispectral image acquisition. doi: 10.1117/12.748178

Keywords

  • Particles
  • Particle filters
  • Detection and tracking algorithms
  • Filtering (signal processing)
  • RGB color model
  • Electronic filtering
  • Algorithm development
  • Cameras
  • Motion models
  • Picosecond phenomena