Fast Subsequence Matching in Motion Capture Data
| Authors | |
|---|---|
| Year of publication | 2017 |
| Type | Article in Proceedings |
| Conference | 21st European Conference on Advances in Databases and Information Systems |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/978-3-319-66917-5_5 |
| Field | Informatics |
| Keywords | subsequence matching; motion capture data; content-based retrieval; similarity measure; segmentation; indexing |
| Description | Motion capture data digitally represent human movements by sequences of body configurations in time. Subsequence matching in such spatio-temporal data is difficult as query-relevant motions can vary in lengths and occur arbitrarily in a very long motion. To deal with these problems, we propose a new subsequence matching approach which (1) partitions both short query and long data motion into fixed-size segments that overlap only partly, (2) uses an effective similarity measure to efficiently retrieve data segments that are the most similar to query segments, and (3) localizes the most query-relevant subsequences within extended and merged retrieved segments in a four-step postprocessing phase. The whole retrieval process is effective and fast in comparison with related work. A real-life 68-minute data motion can be searched in about 1s with the average precision of 87.98% for 5-NN queries. |
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