With the growing availability of wearable technology, video recording devices have become so intimately tied to individuals, that they are able to record the movements of users' hands, making hand-based applications one the most explored area in First Person Vision (FPV). In particular, hand pose recognition plays a fundamental role in tasks such as gesture and activity recognition, which in turn represent the base for developing human-machine interfaces or augmented reality applications. In this work we propose a graph-based representation of hands seen from the point of view of the user, obtained through the shape-fitting capability of a modified Instantaneous Topological Map. Spectral analysis of the graph Laplacian allows to arrange eigenvalues in vectors of features, which prove to be discriminative in classifying the considered hand poses.
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|Titolo:||Hand pose recognition in First Person Vision through graph spectral analysis|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||04.01 - Contributo in atti di convegno|