Unsupervised Analysis of Human Behavior Based on Manifold Learning

Abstract

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.