Fast stochastic optimization for articulated structure tracking

摘要

Recently, an optimization approach for fast visual tracking of articulated structures based on stochastic meta-descent (SMD) [7] has been presented. SMD is a gradient descent with local step size adaptation that combines rapid convergence with excellent scalability. Stochastic sampling helps to avoid local minima in the optimization process. We have extended the SMD algorithm with new features for fast and accurate tracking by adapting the different step sizes between as well as within video frames and by introducing a robust cost function, which incorporates both depths and surface orientations. The advantages of the resulting tracker over state-of-the-art methods are supported through 3D hand tracking experiments. A realistic deformable hand model reinforces the accuracy of our tracker.