Adaptive unscented Kalman filters applied to visual tracking
Qichuan Ding, Xingang Zhao, Jianda Han · 2012
The classic Bays filters applied to model-based visual tracking suffers from high computation complexity and performance degradation when the inaccurate priori knowledge is involved. In order to improve tracking real-time and accuracy, two kinds of adaptive unscented Kalman filters (AUKFs), named the MIT-based AUKF and the master-slave-structure AUKF, respectively, are proposed to estimate the 3-D rigid-body motion from sequential images. The filters use certain feature points' image coordinates as input data to estimate the position and orientation of the object at each instant when an image is captured, and to recover the velocity and angular velocity of the object between consecutive frames. Experimental results show that both the AUKFs can improve estimation real-time and accuracy in visual tracking.