Effective and Efficient Similarity Searching in Motion Capture Data

This publication doesn't include Faculty of Arts. It includes Faculty of Informatics. Official publication website can be found on muni.cz.



Type Article in Periodical
Magazine / Source Multimedia Tools and Applications
MU Faculty or unit

Faculty of Informatics

Doi http://dx.doi.org/10.1007/s11042-017-4859-7
Field Informatics
Keywords Motion capture data retrieval;Effective similarity measure;Efficient indexing;k-NN query;Motion image;Convolutional neural network;Fixed-size motion feature
Description Motion capture data describe human movements in the form of spatio-temporal trajectories of skeleton joints. Intelligent management of such complex data is a challenging task for computers which requires an effective concept of motion similarity. However, evaluating the pair-wise similarity is a difficult problem as a single action can be performed by various actors in different ways, speeds or starting positions. Recent methods usually model the motion similarity by comparing customized features using distance-based functions or specialized machine-learning classifiers. By combining both these approaches, we transform the problem of comparing motions of variable sizes into the problem of comparing fixed-size vectors. Specifically, each rather-short motion is encoded into a compact visual representation from which a highly descriptive 4,096-dimensional feature vector is extracted using a fine-tuned deep convolutional neural network. The advantage is that the fixed-size features are compared by the Euclidean distance which enables efficient motion indexing by any metric-based index structure. Another advantage of the proposed approach is its tolerance towards an imprecise action segmentation, the variance in movement speed, and a lower data quality. All these properties together bring new possibilities for effective and efficient large-scale retrieval.
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