SUBMITTED BY:
DEVENDER BUDHWAR
SAHIL SANDHU
AMIT KUMAR KARNA

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Introduction

2D Target Tracking Using Kalman Filter

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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

The Kalman filter is a recursive estimator. This means that only the estimated state from the previous time step and the current measurement are needed to compute the estimate for the current state. In contrast to batch estimation techniques, no history of observations and/or estimates is required. It is unusual in being purely a time domain filter; most filters (for example low-pass filter) are formulated in the frequency domain and then transformed back to the time domain for implementation.

submitted by:
Sahil Sandhu
Devender Budhwar
Amit Kumar Karna

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[attachment=5441]

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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. Moreover, the parallel extended Kalman filter (PEKF) which consists of a parallel bank of EKF’s, is often encountered in practice.
In the block-based motion-compensated prediction approaches, the most common procedure is the block- matching technique. Given a macro block in the current frame, the objective is to determine the block in a reference frame (one of the past for forward motion estimation or one of the futures for backward motion estimation) in a certain search area that “best” matches the current macro block according to a specific criterion. In most coding systems, the “best” match is found using the mean absolute error (MAE) criterion. There are several well known algorithms that perform the block matching motion estimation, among them being the full search algorithm (FSA) that determines the motion vector of a macro block by computing the MAE at each location in the search area. This is the simplest method, it provides the best performance, but at a very high computational cost.
To reduce these computational requirements, several heuristic search strategies have been developed, as for example the two-dimensional logarithmic search, the parallel one-dimensional search, etc [3-51. These are often referred to as fast search algorithms. In fast algorithms the procedures applied for motion estimation are of significantly lower complexity, but yield a suboptimal solution in the sense that they may not avoid the convergence of the MAE cost function to a local minimum instead of the global one. Lately, some new fast strategies as well as motion estimation have been proposed. But, very few applications are available of Kalman filtering for the estimation of motion vectors.
This paper proposes a motion estimation using Kalman filtering to improve the motion vector estimates resulting from both the conventional three step algorithm (TSA) based Kalman filter proposed in Section 2 introduces the state-space representation for the motion vector.