Hand Gesture Recognition with Leap Motion
This addresses the problem of robust hand gesture recognition for human-computer interaction applications, but it appears incremental as it builds on existing methods with new sensor data.
The paper tackles hand gesture recognition using Leap Motion Controller data by extracting features from tracking data and sensor images, feeding them into a multi-class SVM classifier with dimension reduction and feature weighted fusion. The result is a model that is much more accurate than previous work, though no concrete numbers are provided.
The recent introduction of depth cameras like Leap Motion Controller allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with Leap Motion Controller. A series of features are extracted from Leap Motion tracking data, we feed these features along with HOG feature extracted from sensor images into a multi-class SVM classifier to recognize performed gesture, dimension reduction and feature weighted fusion are also discussed. Our results show that our model is much more accurate than previous work.