基于流形假设的骨架序列动作识别算法

doi:10.12066/j.issn.1007-2861.2316

Abstract

Abstract:

Skeletal data are obtained by encoding the spatial geometricposition of the action, which can prevent the interference ofredundant background information. It is one of the commonly useddata types in the field of action recognition. The existing reviewof action recognition related to skeletal data is mainly dividedinto the classical skeletal data representation and the applicationof skeletal action recognition based on deep learning. Compared withthe action recognition methods based on the traditional Euclideanmetric, manifolds provide an important mathematical tool for abetter study of nonlinear structures. However, there is still a lackof summaries about action recognition from skeletal data using themanifold assumption. Therefore, starting from the four steps ofskeleton representation -- trajectory temporal alignment, actionsequence characterization, and action classification -- thisarticle systematically summarizes the action recognition work basedon the manifold assumption, and compares the performance of eachwork on the benchmark datasets. Finally, according to the currentdevelopment trend of action recognition, further improvement of themanifold assumption in thedirection of action recognition is prospected.

Key words: action recognition, manifold assumption, skeleton representation, trajectory temporal misalignment, shape space

CLC Number:

PENG Yaxin, ZHAO Qian. Skeleton-based action recognition by manifold assumption[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(2): 179-200.

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Skeleton-based action recognition by manifold assumption

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