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• W2804573241 abstract "We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the ‘motion flux’, a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis." @default.
• W2804573241 created "2018-06-01" @default.
• W2804573241 creator A5012726788 @default.
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• W2804573241 date "2019-04-01" @default.
• W2804573241 modified "2023-10-18" @default.
• W2804573241 title "Discovery and recognition of motion primitives in human activities" @default.
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• W2804573241 doi "https://doi.org/10.1371/journal.pone.0214499" @default.
• W2804573241 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6443174" @default.
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• W2804573241 hasPublicationYear "2019" @default.
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