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Multi-view Multi-stance Gait Identification
Maodi Hu; Yunhong Wang; Zhaoxiang Zhang; De Zhang
Conference NameIEEE International Conference on Image Processing
Source PublicationICIP 2011
Conference Date11-14 September 2011
Conference PlaceBrussels, Belgium
AbstractView transformation in gait analysis has attracted more and more attentions recently. However, most of the existing methods are based on the entire gait dynamics, such as Gait Energy Image (GEI). And the distinctive characteristics of different walking phases are neglected. This paper proposes a multi-view multi-stance gait identification method using unified multi-view population Hidden Markov Models (pHMM-s), in which all the models share the same transition probabilities. Hence, the gait dynamics in each view can be normalized into fixed-length stances by Viterbi decoding. To optimize the view-independent and stance-independent identity vector, a multi-linear projection model is learned from tensor decomposition. The advantage of using tensor is that different types of information are integrated in the final optimal solution. Extensive experiments show that our algorithm achieves promising performances of multi-view gait identification even with incomplete gait cycles.
KeywordNormalized Dynamics Multi-view Multi-stance Gait Identification
Document Type会议论文
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Maodi Hu,Yunhong Wang,Zhaoxiang Zhang,et al. Multi-view Multi-stance Gait Identification[C],2011.
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