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