Human-Computer Interaction Using Robust Gesture Recognition

Abstract

We present a detector cascade for robust real-time tracking of hand movements on consumer-level hardware. We adapt existing detectors to our setting: Haar, CAMSHIFT, shape detector, skin detector. We use all these detectors at once. Our main contributions are: first, utilization of bootstrapping: Haar bootstraps itself, then its results are used to bootstrap the other filters; second, the usage of temporal filtering for more robust detection and to remove outliers; third, we adapted the detectors for more robust hand detection. The resulting system produces very robust results in real time. We evaluate both the robustness and the real-time capability.