Real-time gesture recognition using a humanoid robot with a deep neural architecture
Citations Over TimeTop 10% of 2014 papers
Abstract
Dynamic gesture recognition is one of the most interesting and challenging areas of Human-Robot-Interaction (HRI). Problems like image segmentation, temporal and spatial feature extraction and real time recognition are the most promising issues to name in this context. This work proposes a deep neural model to recognize dynamic gestures with minimal image preprocessing and real time recognition in an experimental set up using a humanoid robot. We conducted two experiments with command gestures in an offline fashion and for demonstration in a Human-Robot-Interaction (HRI) scenario. Our results showed that the proposed model achieves high classification rates of the gestures executed by different subjects, who perform them with varying speed. With our additional audio feedback we demonstrate that our system performs in real time.
Related Papers
- → Real-time continuous gesture recognition for natural human-computer interaction(2014)35 cited
- → An Intelligent Smart Home Control Using Body Gestures(2006)18 cited
- → An Intelligent Smart Home Control Using Body Gestures(2006)16 cited
- → Real‐time hand gestures system based on leap motion(2018)11 cited
- Real-Time Gesture Recognition for Dynamic Applications(2015)