2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) 2016
DOI: 10.1109/apsipa.2016.7820717
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Abstract: We present a novel feature descriptor for 3D human action recognition using graph signal processing techniques. A linear subspace is learned using graph total variation and graph Tikhonov regularizers, transforming 3D time derivative information into a representation that is robust against noisy skeleton measurements. The graph total variation regularizer learns an action representation that encourages piece-wise constantness, which helps discriminating between different action classes. Graph Tikhonov regulari… Show more

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(1 citation statement)
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“…Different from the other studies using BP features [8,14,24], we extract a velocity together with a joint position from each joint of the raw skeleton. The velocity represents the variations over the time and has been widely employed in many previous studies, mostly in the handcrafted-feature-based approaches [10,32,34]. It is robust against the speed changes; and accordingly, is effective to discriminate actions with similar distance variations but with different speeds, such as punching and pushing.…”
Section: Feature Representationmentioning
confidence: 99%
“…Different from the other studies using BP features [8,14,24], we extract a velocity together with a joint position from each joint of the raw skeleton. The velocity represents the variations over the time and has been widely employed in many previous studies, mostly in the handcrafted-feature-based approaches [10,32,34]. It is robust against the speed changes; and accordingly, is effective to discriminate actions with similar distance variations but with different speeds, such as punching and pushing.…”
Section: Feature Representationmentioning
confidence: 99%