04-07-2012, 03:08 PM
2D TARGET TRACKING USING KALMAN FILTER
[attachment=26700]
Abstract
It is now quite common in the recursive approaches for motion estimation, to find applications of the Kalman filtering technique both in time and frequency domains. In the block-based approach, very few approaches are available of this technique to refine the estimation of motion vectors resulting from fast algorithms.. This paper proposes an object motion estimation which uses the Kalman filtering technique to improve the motion estimates resulting from both the three step algorithm and Kalman application.
Introduction
In the field of motion estimation for video coding many techniques have been applied. It is now quite common to see the Kalman filtering technique and some of its extensions to be used for the estimation of motion within image sequences.
Particularly in the pixel-recursive approaches, which suit very much the Kalman formulation, one finds various ways of applying this estimation technique both in the time and frequency domains. On a very general perspective, we find use of Kalman filter (KF), which implies linear state-space representations and the extended Kalman filter (EKF), which uses the linearised expressions of non-linear state- space formulations.
[attachment=26700]
Abstract
It is now quite common in the recursive approaches for motion estimation, to find applications of the Kalman filtering technique both in time and frequency domains. In the block-based approach, very few approaches are available of this technique to refine the estimation of motion vectors resulting from fast algorithms.. This paper proposes an object motion estimation which uses the Kalman filtering technique to improve the motion estimates resulting from both the three step algorithm and Kalman application.
Introduction
In the field of motion estimation for video coding many techniques have been applied. It is now quite common to see the Kalman filtering technique and some of its extensions to be used for the estimation of motion within image sequences.
Particularly in the pixel-recursive approaches, which suit very much the Kalman formulation, one finds various ways of applying this estimation technique both in the time and frequency domains. On a very general perspective, we find use of Kalman filter (KF), which implies linear state-space representations and the extended Kalman filter (EKF), which uses the linearised expressions of non-linear state- space formulations.