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Effective and efficient similarity searching in motion capture data


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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This research was supported by GBP103/12/G084.

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Correspondence to Jan Sedmidubsky.

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Sedmidubsky, J., Elias, P. & Zezula, P. Effective and efficient similarity searching in motion capture data. Multimed Tools Appl 77, 12073–12094 (2018).

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  • Motion capture data retrieval
  • Effective similarity measure
  • Efficient indexing
  • k-NN query
  • Motion image
  • Convolutional neural network
  • Fixed-size motion feature