Marginalized Bags of Vectors Kernels on Switching Linear Dynamics for Online Action Recognition

In this paper, we propose a novel kernel computation algorithm between time-series human motion data for online action recognition. The proposed kernel is based on probabilistic models called switching linear dynamics (SLDs). SLD is one of the powerful tools for tracking, analyzing and classifying human complex time-series motion. The proposed kernel incorporates information about the latent variables in SLDs with simplified designing approach called marginalized kernels. The empirical evaluation using real motion data shows that a classifier using SVM with our proposed kernel has much better performance than the classifier with some conventional kernel techniques. Another experiment using walking around motion shows that a classifier with the proposed kernel can properly segment the start and the end of the target action.