Hand gesture recognition is a natural and intuitive method for human-computer interaction and has been an active research area. A wide variety of input devices and techniques have been investigated, and skeleton-based HGR is a popular choice due to its robustness to the background and light variations.
Many skeleton-based HGR systems rely on depth sensors, such as RGB D cameras, which are not nearly as common as RGB cameras on mobile devices. Our HGR, on the other hand, requires only a single RGB camera. It does so by first predicting 3D skeleton key points from a camera image, then running a gesture classifier on the key points.
We design two gesture classifiers with different use cases in mind. The heuristics-based classifier is easier to create and extend, without the need for training data, and more intuitive to develop and troubleshoot. The NN-based classifier is more accurate and precise, especially for borderline cases. It’s also more forgiving of errors in skeleton key points.
Our HGR runs in real-time at 30fps on mainstream mobile devices.
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