Real-Time Gesture Recognition with Virtual Glove Markers
This work addresses the problem of enabling natural human-computer interaction through gestures, but it appears incremental as it builds on existing gesture recognition methods with a focus on real-time performance.
The paper tackles real-time gesture recognition by proposing a computer vision-based system that uses virtual glove markers as input to a deep learning model, achieving effectiveness for applications like telepresence and rehabilitation.
Due to the universal non-verbal natural communication approach that allows for effective communication between humans, gesture recognition technology has been steadily developing over the previous few decades. Many different strategies have been presented in research articles based on gesture recognition to try to create an effective system to send non-verbal natural communication information to computers, using both physical sensors and computer vision. Hyper accurate real-time systems, on the other hand, have only recently began to occupy the study field, with each adopting a range of methodologies due to past limits such as usability, cost, speed, and accuracy. A real-time computer vision-based human-computer interaction tool for gesture recognition applications that acts as a natural user interface is proposed. Virtual glove markers on users hands will be created and used as input to a deep learning model for the real-time recognition of gestures. The results obtained show that the proposed system would be effective in real-time applications including social interaction through telepresence and rehabilitation.