There-Dimensional Hand Tracking Algorithm Characterized by Multi-model Fusion

Three-dimensional hand tracking is important for gesture interaction to achieve high-precision 3D hand tracking in real-time.Targeting on high accuracy of hand tracking, a novel particle filtering algorithm by fusing multiple models of prediction is presented in this paper.First, with the analysis of the virtual oven system based on data glove, we construct a prediction model of human hand behavior based on cognitive psychology, by combining the behavior understanding and description of hand.Secondly, by constructing the hand motion model using the data in hand tracking process, and by applying Sigma point principle to the motion model data, we can get the local analyzing prediction model.Finally, we fuse the two models into a model according to the similarity with the current frame image, which is the state prediction model in the process of particle filter.Compared with the classic annealing algorithm, our algorithm improves the precision of 3D hand tracking.