Querying Complex Spatio-Temporal Sequences in Human Motion Databases

Content-based retrieval of spatio-temporal patterns from human motion databases is inherently nontrivial since finding effective distance measures for such data is difficult. These data are typically modelled as time series of high dimensional vectors which incur expensive storage and retrieval cost as a result of the high dimensionality. In this paper, the authors abstract such complex spatio-temporal data as a set of frames which are then represented as high dimensional categorical feature vectors. New distance measures and queries for high dimensional categorical time series are then proposed and efficient query processing techniques for answering these queries are developed.