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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50054

Title: Unscented Kalman filtering for single camera based motion and shape estimation.
Authors: Dah-Jing Jwo
Chien-Hao Tseng
Jen-Chu Liu
Hsien-Der Lee
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Keywords: motion
optical flow
shape
unscented Kalman filter
Date: 2011-07
Issue Date: 2018-09-14
Publisher: Sensors
Abstract: 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.
Relation: 11(8) pp.7437-54
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50054
Appears in Collections:[通訊與導航工程學系] 期刊論文

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