Efficient and effective query processing of complex human motion sequences

Abstract

Spatio-temporal sequences are used for recording spatial and temporal changes in information. Such information comes in the form of spatio-temporal sequences, and may represent important phenomena and semantics. In this thesis, we focus on one type of spatio-temporal sequences -- human motion sequences -- which are typically large in volume and computationally costly to match with each other. We seek to address the challenges of efficiently and effectively querying and managing a large set of human motion sequences, with the aim of applying the solutions in areas including animation design, clinical gait analysis and human behavior recognition. In particular, we consider the following aspects of the problem: effective matching of human motion sequences, efficient subsequence matching, and subsequence join of human motion sequences. Our solutions on subsequence matching and subsequence join techniques over human motion sequences can be applied to managing a large set of human motion sequences.