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.
Highlights
Human-machine interaction is entering a new era, with computers altering the way they respond to human stimuli
Feature pre-processing is strongly related to the utilized cue, in problems related to human activity recognition
For new data vectors, no local sub-manifold unfolding is necessary and, for inference, simple matrix operations are needed. This is of great significance, since it allows for real-time action recognition and constitutes the proposed method appropriate for online evaluation of whether the projection of multiple modality features over the course of an action is close to the subspace classes of a trained model
Summary
Human-machine interaction is entering a new era, with computers altering the way they respond to human stimuli. Wearable inertial measurement sensors [11], robust video processing algorithms [1], infrared and depth sensors [7] and audio [27] are only a few of the cues available for understanding human activity These advances brought automatic action recognition to the front-end in many applications, ranging from entertainment to health-care systems. A low-dimensional representation of large dimensionality feature vectors is utilized, by following a landmark-based spectral analysis scheme In this way, low-dimensional subspaces, encoding valuable information, are built, while new, unknown actions are projected on them. The proposed technique builds on authors’ preliminary work on Microsoft kinect-based activity recognition based on spectral analysis, [3] where results were presented on the single-modality case of only depth data, while inter- and intra-individual sub-actions were not considered and experiments were limited to a single scenario.
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