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V. Ntouskos, P. Papadakis, and F. Pirri (2012)

A Comprehensive Analysis of Human Motion Capture Data for Action Recognition

In: Proceedings of the International Conference on Computer Vision Theory and Applications.

In this paper, we present an analysis of human motion that can assist the recognition of human actions irrespective of the selection of particular features. We begin with an analysis on the entire set of preclassified motions in order to derive the generic characteristics of articulated human motion and complement the analysis by a more detailed inter-class analysis. The statistical analysis concerns features that describe the significance-contribution of the human joints in performing an action. Furthermore, we adopt a hierarchical analysis on the human body itself in the study of different actions, by grouping joints that share common characteristics. We present our experiments on standard databases for human motion capture data as well as a new commercial dataset with additional classes of human motion and highlight certain interesting results.
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