標題: | Unsupervised Analysis of Human Behavior Based on Manifold Learning |
作者: | Liang, Yu-Ming Shih, Sheng-Wen Shih, Arthur Chun-Chieh Liao, Hong-Yuan Mark Lin, Cheng-Chung 資訊工程學系 Department of Computer Science |
公開日期: | 2009 |
摘要: | In this paper, we propose a framework for unsupervised analysis of human behavior based on manifold learning. First, a pairwise human posture distance matrix is calculated from a training action sequence. Then, the isometric feature mapping (Isomap) algorithm is applied to construct a low-dimensional structure from the distance matrix. The data points in the Isomap space are consequently represented as a time-series of low-dimensional points. A temporal segmentation technique is then applied to segment the time series into subseries corresponding to atomic actions. Next, a dynamic time warping (DTW) approach is applied for clustering atomic action sequences. Finally, we use the clustering results to learn and classify atomic actions using the nearest neighbor rule. Experiments conducted on real data demonstrate the efficacy of the proposed method. |
URI: | http://hdl.handle.net/11536/16445 |
ISBN: | 978-1-4244-3827-3 |
期刊: | ISCAS: 2009 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-5 |
起始頁: | 2605 |
結束頁: | 2608 |
顯示於類別: | 會議論文 |