Abstract

Traditional image based hand tracking uses a single Kalman filter to estimate and predict the hand state (position, velocity, and acceleration). However, this approach may fail in the case of large maneuvers and cluttered measurements. In this paper we propose to use the interacting multiple model (IMM) filter to catch a maneuver and the probabilistic data association (PDA) method to process noisy measurements and false alarms. A theoretical framework of image based hand tracking by IMM-PDA is set up. Experiment results from several video segments show that IMM-PDA can successfully track hand motions in a natural conversational environment.

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