Duodepth: Static Gesture Recognition Via Dual Depth Sensors
This addresses occlusion issues in gesture recognition for human-computer interaction, but it is incremental as it builds on existing depth sensing and neural network methods.
The paper tackled the problem of occlusion in static gesture recognition by using two depth cameras, resulting in a 39.2% reduction in misclassification with fused point clouds and 53.4% with a dual PointNet architecture compared to a single-camera approach.
Static gesture recognition is an effective non-verbal communication channel between a user and their devices; however many modern methods are sensitive to the relative pose of the user's hands with respect to the capture device, as parts of the gesture can become occluded. We present two methodologies for gesture recognition via synchronized recording from two depth cameras to alleviate this occlusion problem. One is a more classic approach using iterative closest point registration to accurately fuse point clouds and a single PointNet architecture for classification, and the other is a dual Point-Net architecture for classification without registration. On a manually collected data-set of 20,100 point clouds we show a 39.2% reduction in misclassification for the fused point cloud method, and 53.4% for the dual PointNet, when compared to a standard single camera pipeline.