Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
527883 | Computer Vision and Image Understanding | 2011 | 15 Pages |
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.
Research highlights► We systematically examine multifactor approaches to human pose feature extraction. ► A multilinear approach and a multifactor Gaussian process approach have been adopted and compared. ► We find that the multilinear approach extracts better pose features for movement recognition.