Real-time and robust grasping detection

Depth-based gesture cameras provide a promising and novel way to interface with computers. Nevertheless, this type of interaction remains challenging due to the complexity of finger interactions and the under large viewpoint variations. Existing middleware such as Intel Perceptual Computing SDK (PCSDK) or SoftKinetic IISU can provide abundant hand tracking and gesture information. However, the data is too noisy (Fig. 1, left) for consistent and reliable use in our application. In this work, we present a filtering approach that combines several features from PCSDK to achieve more stable hand openness and supports grasping interactions in virtual environments. Support vector machine (SVM), a machine learning method, is used to achieve better accuracy in a single frame, and Markov Random Field (MRF), a probability theory, is used to stabilize and smooth the sequential output. Our experimental results verify the effectiveness and the robustness of our method.