A singular value decomposition updating algorithm for subspace tracking

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Video tracking is the process of locating a moving object (or multiple objects) over time using a camera.

It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging and video editing.

In addition to the new algorithms, we show how the geometrical framework gives penetrating new insights allowing us to create, understand, and compare algorithms.

The theory proposed here provides a taxonomy for numerical linear algebra algorithms that provide a top level mathematical view of previously unrelated algorithms.

Typical examples are adaptive beamforming, direction finding, spectral analysis, pattern recognition, etc. Typical examples are adaptive beamforming, direction finding, spectral analysis, pattern recognition, etc. In =-=[12]-=-, it has been shown how an SVD updating algorithm can be derived by combining QR updating with a Jacobi-type SVD procedure applied to the triangular factor. At that stage, none of the existing estimation tools are adequate, as they either assume a fully specified execution order or ign ..." Abstract — A novel storage requirement estimation methodology is presented for use in the early system design phases when the data transfer ordering is only partly fixed.

In certain signal processing applications it is required to compute the null space of a matrix whose rows are samples of a signal with p components.

For a given matrix H which has d singular values larger than e, we find all rank d approximants H such that ..." This paper describes a much simpler generalized Schur-type algorithm to compute similar low-rank approximants.

The updating technique can be run on a linear array of p processors in O(p) time. Introduction Many problems in digital signal processing require the computation of an approximate null space of an n \Theta p matrix A whose rows represent samples of a signal (see [9] for examples and references). The usual tool for doing this is the singular value decomposition.However, the singular value decomposition has the drawback that it requires O(p 3 ) operations to recompute when a new sample arrives. Finally, an error analysis is performed, proving that the algorithm is stable, when supplemented with a Jacobi-type re-orthogonalization procedure, which can easily be incorporated into the updating scheme. ABSTRACT: In real-time data-intensive multimedia processing applications, data transfer and storage significantly influence, if not dominate, all the major cost parameters of the design space—namely energy consumption, performance, and chip area.This paper presents an electronic design automation (EDA) methodology for the high-level design of hierarchical memory architectures in embedded data-intensive applications, mainly in the area of multidimensional signal processing.

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