Real-time hand tracking by invariant hough forest detection
This paper proposes a robust real-time hand tracking approach by combining a discriminative random forest classifier with generative color based cues using a particle filter. The proposed detector is scale and rotation invariant and is able to overcome ambiguities and local maxima in the color based likelihood function in real-time. A new hand tracking dataset with manually annotated groundtruths is created and made freely available for research purposes. Thorough evaluation shows the robustness and advantages of our proposal compared to other state of the art object tracking methods.