title: Unscented Kalman filtering for single camera based motion and shape estimation.
abstract: Accurate estimation of the motion and shape of a moving object is a challenging task due to great variety of noises present from sources such as electronic components and the influence of the external environment, etc. To alleviate the noise, the filtering/estimation approach can be used to reduce it in streaming video to obtain better estimation accuracy in feature points on the moving objects. To deal with the filtering problem in the appropriate nonlinear system, the extended Kalman filter (EKF), which neglects higher-order derivatives in the linearization process, has been very popular. The unscented Kalman filter (UKF), which uses a deterministic sampling approach to capture the mean and covariance estimates with a minimal set of sample points, is able to achieve at least the second order accuracy without Jacobians' computation involved. In this paper, the UKF is applied to the rigid body motion and shape dynamics to estimate feature points on moving objects. The performance evaluation is carried out through the numerical study. The results show that UKF demonstrates substantial improvement in accuracy estimation for implementing the estimation of motion and planar surface parameters of a single camera.
authors.affiliation: Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, 2 Pei-Ning Rd, Keelung 202-24, Taiwan. djjwo@mail.ntou.edu.tw
authors.forename: Dah-Jing
authors.initials: DJ
authors.lastname: Jwo
authors.forename: Chien-Hao
authors.initials: CH
authors.lastname: Tseng
authors.forename: Jen-Chu
authors.initials: JC
authors.lastname: Liu
authors.forename: Hsien-Der
authors.initials: HD
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003201:Computers; D004867:Equipment Design; D007091:Image Processing, Computer-Assisted; D009038:Motion; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D011336:Probability; D015203:Reproducibility of Results; D013269:Stochastic Processes