Kernelized Temporal Cut for Online Temporal Segmentation and Recognition

Abstract

We address the problem of unsupervised online segmenting human motion sequences into different actions. Kernelized Temporal Cut (KTC), is proposed to sequentially cut the structured sequential data into different regimes. KTC extends previous works on online change-point detection by incorporating Hilbert space embedding of distributions to handle the nonparametric and high dimensionality issues. Based on KTC, a realtime online algorithm and a hierarchical extension are proposed for detecting both action transitions and cyclic motions at the same time. We evaluate and compare the approach to state-of-the-art methods on motion capture data, depth sensor data and videos. Experimental results demonstrate the effectiveness of our approach, which yields realtime segmentation, and produces higher action segmentation accuracy. Furthermore, by combining with sequence matching algorithms, we can online recognize actions of an arbitrary person from an arbitrary viewpoint, given realtime depth sensor input.