Dynamic Data Driven Coupling of Continuous and Discrete Methods for 3D Tracking*
Dimitris Metaxas and Gabriel Tsechpenakis
Abstract. We present a new framework for robust 3D tracking, using a dynamic data driven coupling of continuous and discrete methods to overcome their limitations. Our method uses primarily the continuous-based tracking which is replaced by the discrete one, to obtain model re-initializations when necessary. We use the error in the continuous tracking to learn off-line, based on SVMs, when the continuous-based tracking fails and switch between the two methods. We develop a novel discrete method for 3D shape configuration estimation, which utilizes both frame and multi-frame features, taking into account the most recent input frames, using a time-window. We therefore overcome the error accumulation over time, that most continuous methods suffer from and simultaneously reduce the discrete methodís complexity and prevent possible multiple solutions in shape estimation. We demonstrate the power of our framework in complex hand tracking sequences with large rotations, articulations, lighting changes and occlusions.
*This research has been funded by an NSF ITR/NGS-0313134 and an NSF ITR-[ASE+ECS]-0428231 Collaborative Project to the first author.
LNCS 3515, pp. 712-720.