Unsupervised categorization of human motion sequences

Abstract

Multivariate timeseries become a popular data form to represent images, that are used as suitable inputs to higherlevel recognition processes. We present a novel cluster analysis based on timeseries structure to identify similar human motion sequences. To clustering sequences, the movement silhouettes from video were transformed into low-dimensional multivariate timeseries, then further converted into vectors based on their structure in a finite-dimensional Euclidean space. The identification and selection of structural metrics for human motion sequences were highlighted to demonstrate that these statistical features are generic but also problem dependent. Various clustering algorithms were used to demonstrate the effectiveness and simplicity using real data sets.