Hybrid particle and Kalman filtering for pupil tracking in active IR illumination gaze tracking system
Date of Issue2014
School of Electrical and Electronic Engineering
A novel pupil tracking method is proposed by combining particle filtering and Kalman filtering for the fast and accurate detection of pupil target in an active infrared source gaze tracking system. Firstly, we utilize particle filtering to track pupil in synthesis triple-channel color map (STCCM) for the fast detection and develop a comprehensive pupil motion model to conduct and analyze the randomness of pupil motion. Moreover, we built a pupil observational model based on the similarity measurement with generated histogram to improve the credibility of particle weights. Particle filtering can detect pupil region in adjacent frames rapidly. Secondly, we adopted Kalman filtering to estimate the pupil parameters more precisely. The state transitional equation of the Kalman filtering is determined by the particle filtering estimation, and the observation of the Kalman filtering is dependent on the detected pupil parameters in the corresponding region of difference images estimated by particle filtering. Tracking results of Kalman filtering are the final pupil target parameters. Experimental results demonstrated the effectiveness and feasibility of this method.
DRNTU::Engineering::Electrical and electronic engineering
Mathematical problems in engineering
© 2014 Jian-nan Chi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.