Real-Time Arm Tracking System Using Example-based Matching and Local Optimization

In this paper, we present an approach to infer the arm pose in real-time for human-computer interaction. The approach is divided into example-based matching and local optimization. We build a database using synthesized disparity maps as examples and use them to compare with the input disparity map. The pose of the best match is used as the initial condition for local optimization. We implement the local optimization by applying physical forces to attract a dynamic arm model. The arm model contains prismatic joints that can alleviate the problem from imprecision model size. The example-based matching can cover a large range of motion and provide a good initial condition which prevents arm model from being trapped into local maxima, and the local optimization provides a better estimation of pose that compensates for the insufficient pose resolution of examples.