Gesture recognition provides real-time data to a computer to make it fulfill the user’s commands. Motion sensors in a device can track and interpret gestures, using them as the primary source of data input. A majority of gesture recognition solutions feature a combination of 3D depth-sensing cameras and infrared cameras together with machine learning systems. Machine learning algorithms are trained based on labeled depth images of hands, allowing them to recognize hand and finger positions.
- Detection. With the help of a camera, a device detects hand or body movements, and a machine learning algorithm segments the image to find hand edges and positions.
- Tracking. A device monitors movements frame by frame to capture every movement and provide accurate input for data analysis.
- Recognition. The system tries to find patterns based on the gathered data. When the system finds a match and interprets a gesture, it performs the action associated with this gesture. Feature extraction and classification in the scheme below implements the recognition functionality.
