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• W2962912288 abstract "In this paper, we propose a novel framework for dynamical analysis of human actions from 3D motion capture data using topological data analysis. We model human actions using the topological features of the attractor of the dynamical system. We reconstruct the phase-space of time series corresponding to actions using time-delay embedding, and compute the persistent homology of the phase-space reconstruction. In order to better represent the topological properties of the phase-space, we incorporate the temporal adjacency information when computing the homology groups. The persistence of these homology groups encoded using persistence diagrams are used as features for the actions. Our experiments with action recognition using these features demonstrate that the proposed approach outperforms other baseline methods." @default.
• W2962912288 created "2019-07-30" @default.
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• W2962912288 date "2016-09-01" @default.
• W2962912288 modified "2023-09-27" @default.
• W2962912288 title "Persistent homology of attractors for action recognition" @default.
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• W2962912288 doi "https://doi.org/10.1109/icip.2016.7533141" @default.
• W2962912288 hasPublicationYear "2016" @default.
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