Robust Hand Tracking with Hough Forest and Multi-cue Flocks of Features

Robust hand tracking is highly demanded for many real-world applications relevant to human machine interface. However, current methods achieve no satisfactory robustness in real environments. In this paper a novel hand tracking method was proposed integrating online Hough Forest and Flocks-of-Features tracking. Skin color was integrated in the Hough Forest framework to gain more robustness against drastic hand appearance and pose changes, especially against partial occlusions. Also a novel multi-cue Flocks-of-Features tracking algorithm based on computer graphics was integrated in to enhance the framework's robustness against distractors and background clutter. Additionally, recovery from tracking failure was addressed. Lots of experiments were carried out to evaluate our method, also to compare it with CAMShift, Hough Forest tracker, and the original Flocks-of-Features Tracker, and showed the effectiveness of our method.