Vision-Based Hand Motion Capture Using Genetic Algorithm

Abstract

Abstract3D hand motion capture plays an important role in multi-modal human-computer interfaces. Existing vision-based approaches mainly include two directions: model-based optimization framework and appearance-based learning approach. The main obstacle to handle with human hand motion capture is the high dimensionality associated with a full degree-of-freedom (DOF) articulated model. In this paper, a novel vision-based 3D hand motion capture algorithm is proposed. It views hand pose estimation and motion tracking as search problems and utilizes genetic algorithm (GA). Firstly, a learning integrating with optimization approach is introduced to estimate initial hand pose in 3D model based framework. And then a motion tracking method using GA-based particle filter (PF) is proposed to deal with the tracking problem in high-dimensional and multi-modal state space. Experimental results show that present approach significantly improves performance of motion tracking, especially in high-dimensional configuration space.KeywordsParticle FilterMotion TrackingInvariant MomentChamfer DistanceAutomatic InitializationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.