Similarity Searching in Long Sequences of Motion Capture Data

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Authors

SEDMIDUBSKÝ Jan ELIÁŠ Petr ZEZULA Pavel

Year of publication 2016
Type Article in Proceedings
Conference Proceedings of 9th International Conference on Similarity Search and Applications (SISAP 2016), LNCS 9939
MU Faculty or unit

Faculty of Informatics

Citation
Doi http://dx.doi.org/10.1007/978-3-319-46759-7_21
Field Informatics
Keywords motion capture data; similarity search; subsequence search; multi-level segmentation
Description Motion capture data digitally represent human movements by sequences of body configurations in time. Searching in such spatio-temporal data is difficult as query-relevant motions can vary in lengths and occur arbitrarily in the very long data sequence. There is also a strong requirement on effective similarity comparison as the specific motion can be performed by various actors in different ways, speeds or starting positions. To deal with these problems, we propose a new subsequence matching algorithm which uses a synergy of elastic similarity measure and multi-level segmentation. The idea is to generate a minimum number of overlapping data segments so that there is at least one segment matching an arbitrary subsequence. A non-partitioned query is then efficiently evaluated by searching for the most similar segments in a single level only, while guaranteeing a precise answer with respect to the similarity measure. The retrieval process is efficient and scalable which is confirmed by experiments executed on a real-life dataset.
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