Hand Gesture Recognition From Wrist-Worn Camera for Human–Machine Interaction

Hand Gesture Recognition From Wrist-Worn Camera for Human–Machine Interaction

Blog Article

In this work, we study the ability to use hand gestures for human-machine interaction from wrist-worn sensors.Towards this goal, we design a wrist-worn prototype to capture RGB video stream of hand gestures.Then we built a new wrist-worn gesture dataset (named WiGes) with various subjects in interaction with home appliances in different environments.To the best of our knowledge, this is the first benchmark released for studying vista 5 vl5 hand gestures from a wrist-worn camera.We then evaluate various CNN models for vision-based recognition.

Furthermore, we deeply analyze the models that produce the lick em sticks candy best trade-off between accuracy, memory requirement, and computational cost.We point out that among studied architectures, MoviNet produces the highest accuracy.Then, we introduce a new MoviNet-based two-stream architecture that takes both RGB and optical flow into account.Our proposed architecture increases the Top-1 accuracy by 1.36% and 3.

67% according to two evaluation protocols.Our dataset, baselines, and proposed model analysis give instructive recommendations for human-machine interaction using hand-held devices.