Motion capture data digitally represent human movements by#N#sequences of body configurations in time. Searching in such#N#spatio-temporal data is difficult as query-relevant motions can#N#vary in lengths and occur arbitrarily in the very long data#N#sequence. There is also a strong requirement on effective#N#similarity comparison as the specific motion can be performed#N#by various actors in different ways, speeds or starting#N#positions. To deal with these problems, we propose a new#N#subsequence matching algorithm which uses a synergy of elastic#N#similarity measure and multi-level segmentation. The idea is to#N#generate a minimum number of overlapping data segments so that#N#there is at least one segment matching an arbitrary#N#subsequence. A non-partitioned query is then efficiently#N#evaluated by searching for the most similar segments in a#N#single level only, while guaranteeing a precise answer with#N#respect to the similarity measure. The retrieval process is#N#efficient and scalable which is confirmed by experiments#N#executed on a real-life dataset.
['Jan Sedmidubský', 'Petr Elias', 'Pavel Zezula']
Similarity Searching in Long Sequences of Motion Capture Data