Every form of human gesture has been recognized in the literature as a means of providing natural and intuitive ways to interact with computers across many computer application domains. In this paper we propose a real time gesture recognition approach which uses a depth sensor to extract the initial human skeleton. Then, robust and significant features have been compared and the most unrelated and representative features have been selected and fed to a set of supervised classifiers trained to recognize different gestures. Different problems concerning the gesture initialization, segmentation, and normalization have been considered. Several experiments have demonstrated that the proposed approach works effectively in real time applications.
|Autori interni:||GUARAGNELLA, Cataldo|
|Titolo:||A real time gesture recognition system for Human Computer Interaction|
|Titolo del libro:||Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction: Third IAPR TC3 Workshop, MPRSS 2014, Stockholm, Sweden, August 24, 2014, Revised Selected Papers|
|Data di pubblicazione:||2015|
|Digital Object Identifier (DOI):||10.1007/978-3-319-14899-1_9|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|