Single camera based motion and shape estimation using extended Kalman filtering

Unscented Kalman Filtering for Single Camera Based Motion and Shape Estimation

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...

Pose and Motion Estimation Using Dual quaternion-based Extended Kalman Filtering

Configuration Estimation of Gough-stewart Platforms Using Extended Kalman Filtering

This paper develops extended Kalman filtering algorithms for a generic Gough-Stewart platform assuming realistically available sensors such as length sensors, rate gyroscopes, and accelerometers. The basic idea is to extend existing methods for satellite attitude estimation. The nondeterministic methods are meant to be a practical alternative to existing iterative, deterministic methods for rea...

Motion Detection and Tracking for an AIBO Robot Using Camera Motion Compensation and Kalman Filtering

Motion detection and tracking while moving is a desired ability for any soccer player and in particular for any robot soccer player. For instance, this ability allows the determination of the ball trajectory when the player is moving himself or when he is moving his head, for making or planning a soccer-play. If a robot soccer player should have a similar functionality, then it requires an algo...

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probabili...