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• W2518984908 abstract "Human activity recognition has received a lot of attention recently, mainly thanks to the advancements in sensing technologies and systems’ increasing computational power. However, complexity in human movements, sensing devices’ noise and person-specific characteristics impose challenges that still remain to be overcome. In the proposed work, a novel, multi-modal human action recognition method is presented for handling the aforementioned issues. Each action is represented by a basis vector and spectral analysis is performed on an affinity matrix of new action feature vectors. Using modality-dependent kernel regressors for computing the affinity matrix, complexity is reduced and robust low-dimensional representations are achieved. The proposed scheme supports online adaptivity of modalities, in a dynamic fashion, according to their automatically inferred reliability. Evaluation on three publicly available datasets demonstrates the potential of the approach." @default.
• W2518984908 created "2016-09-23" @default.
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• W2518984908 date "2016-09-19" @default.
• W2518984908 modified "2023-09-26" @default.
• W2518984908 title "Landmark-based multimodal human action recognition" @default.
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• W2518984908 doi "https://doi.org/10.1007/s11042-016-3945-6" @default.
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