Abstract
More and more researchers focus their studies on multi-view activity recognition, because a fixed view could not provide enough information for recognition. In this paper, we use multi-view features to recognize six kinds of gymnastic activities. Firstly, shape-based features are extracted from two orthogonal cameras in the form of R transform. Then a multi-view approach based on Fused HMM is proposed to combine different features for similar gymnastic activity recognition. Compared with other activity models, our method achieves better performance even in the case of frame loss. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Wang, Y., Huang, K., & Tan, T. (2007). Multi-view gymnastic activity recognition with fused HMM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4843 LNCS, pp. 667–677). Springer Verlag. https://doi.org/10.1007/978-3-540-76386-4_63
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