Abstract
In this paper, we systematically examine multifactor approaches to human pose feature extraction and compare their performances in movement recognition. Two multifactor approaches have been used in pose feature extraction, including a deterministic multilinear approach and a probabilistic approach based on multifactor Gaussian process. These two approaches are compared in terms of the degrees of view-invariance, reconstruction capacity, performances in human pose and gesture recognition using real movement datasets. The experimental results show that the deterministic multilinear approach outperforms the probabilistic-based approach in movement recognition.
Original language | English (US) |
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Pages (from-to) | 375-389 |
Number of pages | 15 |
Journal | Computer Vision and Image Understanding |
Volume | 115 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2011 |
Keywords
- Feature extraction
- Gesture recognition
- Multifactor analysis
- Pose recognition
- View invariance
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition