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Ke et al., 2017 - Google Patents

Skeletonnet: Mining deep part features for 3-d action recognition

Ke et al., 2017

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Document ID
16956093283594565768
Author
Ke Q
An S
Bennamoun M
Sohel F
Boussaid F
Publication year
Publication venue
IEEE signal processing letters

External Links

Snippet

This letter presents SkeletonNet, a deep learning framework for skeleton-based 3-D action recognition. Given a skeleton sequence, the spatial structure of the skeleton joints in each frame and the temporal information between multiple frames are two important factors for …
Continue reading at research-repository.uwa.edu.au (PDF) (other versions)

Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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