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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Abstract:
Markerless tracking of hands and fingers is a promising enabler for
human-computer interaction. However, adoption has been limited because of
tracking inaccuracies, incomplete coverage of motions, low framerate, complex
camera setups, and high computational requirements. In this paper, we present a
fast method for accurately tracking rapid and complex articulations of the hand
using a single depth camera. Our algorithm uses a novel detection-guided
optimization strategy that increases the robustness and speed of pose
estimation. In the detection step, a randomized decision forest classifies
pixels into parts of the hand. In the optimization step, a novel objective
function combines the detected part labels and a Gaussian mixture
representation of the depth to estimate a pose that best fits the depth. Our
approach needs comparably less computational resources which makes it extremely
fast (50 fps without GPU support). The approach also supports varying static,
or moving, camera-to-scene arrangements. We show the benefits of our method by
evaluating on public datasets and comparing against previous work.
