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, we 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. We conducted experiments using our proposed distance measures and queries on human motion capture databases. The results indicate that significant improvement on the efficiency of query processing of categorical time series (more than 10,000 times faster than that of the original motion sequences) can be achieved while guaranteeing the effectiveness of the search.