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A Self-updating Template Approach for Human Action Recognition Using Improved DTW |
Changhui Li, Chunming Gao, Guanghua Tan, Songrui Guo |
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Abstract The existing recognition algorithms always need large number of
samples to train models or templates. Nevertheless, they aren't
view-invariant and affected by performing speed and scale variations
of different actors. In this paper, we capture the skeleton
information by Kinect and present a novel approach based on
self-updating template for human action recognition using 4D
Quaternions as a compact representation of postures. We adopt the
new template generation method and update the template if it
satisfies specific conditions. This method solves the above
problems. On the other hand, we improve the decision function of DTW
that the number of alignment paths may have a great influence on
recognition. We tested our approach on our dataset containing 960
action samples and the public MSR Action3D dataset. Compared with Li
et al.'s \cite{ref10}, our approach produced better results on the
most of cases, especially with the increased number of recognition.
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DOI:10.12733/jics20104339 |
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