  Real-time hand articulations tracking is important for many applications such
as interacting with virtual / augmented reality devices or tablets. However,
most of existing algorithms highly rely on expensive and high power-consuming
GPUs to achieve real-time processing. Consequently, these systems are
inappropriate for mobile and wearable devices. In this paper, we propose an
efficient hand tracking system which does not require high performance GPUs. In
our system, we track hand articulations by minimizing discrepancy between depth
map from sensor and computer-generated hand model. We also initialize hand pose
at each frame using finger detection and classification. Our contributions are:
(a) propose adaptive hand model to consider different hand shapes of users
without generating personalized hand model; (b) improve the highly efficient
frame initialization for robust tracking and automatic initialization; (c)
propose hierarchical random sampling of pixels from each depth map to improve
tracking accuracy while limiting required computations. To the best of our
knowledge, it is the first system that achieves both automatic hand model
adjustment and real-time tracking without using GPUs.
