Abstract
The Zernike moments are a set of orthogonal moments, which can describe more details of the target than the Hu moments. The 3D Zernike descriptors are natural extensions of spherical harmonics based descriptors. In this paper, we adopt the 3D Zernike moment to calculate the global feature of the human action, then, classify the image sequences with the Bayes based AdaBoost classifier. Our experiment results show that the 3D Zernike moment is better than geometric moment in human action classification.
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