Abstract
Our goal is automatic recognition of basic human actions, such as stand, sit and wave hands, to aid in natural communication between a human and a computer. Human actions are inferred from human body joint motions, but such data has high dimensionality and large spatial and temporal variations may occur in executing the same action. We present a learning-based approach for the representation and recognition of 3D human action. Each action is represented by a template consisting of a set of channels with weights. Each channel corresponds to the evolution of one 3D joint coordinate and its weight is learned according to the Neyman-Pearson criterion. We use the learned templates to recognize actions based on χ 2 error measurement. Results of recognizing 22 actions on a large set of motion capture sequences as well as several annotated and automatically tracked sequences show the effectiveness of the proposed algorithm.
This research was supported, in part, by the Advanced Research and Development Activity of the U.S. Government under contract No. MDA904-03-C1786
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Lv, F., Nevatia, R., Lee, M.W. (2005). 3D Human Action Recognition Using Spatio-temporal Motion Templates. In: Sebe, N., Lew, M., Huang, T.S. (eds) Computer Vision in Human-Computer Interaction. HCI 2005. Lecture Notes in Computer Science, vol 3766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573425_12
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DOI: https://doi.org/10.1007/11573425_12
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