Unsupervised Human Motion Analysis Using Automatic Label Tree
文献类型：会议论文
| 作者 | Kui Jia; Xie Wuyuan |
| 出版日期 | 2009 |
| 会议名称 | 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009 |
| 会议地点 | Hong Kong |
| 英文摘要 | Giving different human motions, there may be similar motion features in some body components, e.g., the motion features of arms when performing jogging and running, which are thus less discriminative for motion classification. In this paper, we consider counting less on these body components that have less discriminative information amongst different human motions. To this end, we present a new topic model, probabilistic latent semantic analysis based on multiple bags (M-PLSA), in which not all body components are considered equally important, i.e.,motion features of less discriminative components are made less use of so that their contributions for classification are reduced. We use sparse spatio-temporal features extracted from videos to create visual words which are later assigned to different body components that they are detected from, so that co-occurrence matrices of different components can be calculated based on their corresponding vocabularies. Such label task can beautomatically fulfilled by using the query visual words, i.e., words whose component labels are unknown, to traverse an automatic label tree (ALT) that grows from the training words with component labels. We show the performance of our approach on KTH dataset |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/2457]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2009 |
| 推荐引用方式 GB/T 7714 | Kui Jia,Xie Wuyuan. Unsupervised Human Motion Analysis Using Automatic Label Tree[C]. 见:2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009. Hong Kong. |
入库方式： OAI收割
来源：深圳先进技术研究院
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