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.
Original language | English |
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Pages (from-to) | 4505-4521 |
Number of pages | 17 |
Journal | Multimedia Tools and Applications |
Volume | 76 |
Issue number | 3 |
DOIs | |
Publication status | Published - Feb 2017 |
Keywords
- Spectral clustering
- Human action recognition
- Multimodal fusion
- HUMAN MOTION
- SEQUENCES
- VIDEO