Multi-view Multi-stance Gait Identification
文献类型：会议论文
| 作者 | Maodi Hu; Yunhong Wang; Zhaoxiang Zhang ; De Zhang
|
| 出版日期 | 2011-09-11 |
| 会议日期 | 11-14 September 2011 |
| 会议地点 | Brussels, Belgium |
| 关键词 | Normalized Dynamics Multi-view Multi-stance Gait Identification |
| 英文摘要 | View 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. |
| 会议录 | ICIP 2011
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13281]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Zhaoxiang Zhang |
| 推荐引用方式 GB/T 7714 | Maodi Hu,Yunhong Wang,Zhaoxiang Zhang,et al. Multi-view Multi-stance Gait Identification[C]. 见:. Brussels, Belgium. 11-14 September 2011. |
入库方式： OAI收割
来源：自动化研究所
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