Evaluating hardware design principles for the development of computer based systems

Brielmann, M.; Rammig, J.-F.

doi:10.1109/ecbs.1996.494525

Cited by 2 publications

(1 citation statement)

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“…In the main, an abstraction mechanism hides many implementation details at each design stage. This abstraction-based approach has been well known for many years in computer science, and it has been successfully applied to different areas such as programming language design (Shaw, 1980), program development (Kramer, 2007), software engineering (Kramer & Hazzan, 2006), hardware design (Brielmann & Rammig, 1996) etc. We progressively refine the initial general algorithm and the description of the problem domain towards the solution, in two coarse-to-fine steps: first from the general to the specific in the algorithm definition (at the algorithm level), and later from the abstract to the concrete when introducing the problem context (at the application level).…”

Section: Proposed Framework: Heuristic Particle Filtermentioning

confidence: 99%

Heuristic particle filter: applying abstraction techniques to the design of visual tracking algorithms

2010

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Many real-world visual tracking applications have a high dimensionality, i.e. the system state is defined by a large number of variables. This kind of problem can be modelled as a dynamic optimization problem, which involves dynamic variables whose values change in time. Most applied research on optimization methods have focused on static optimization problems but these static methods often lack explicit adaptive methodologies. Heuristics are specific methods for solving problems in the absence of an algorithm for formal proof. Metaheuristics are approximate optimization methods which have been applied to more general problems with significant success. However, particle filters are Monte Carlo algorithms which solve the sequential estimation problem by approximating the theoretical distributions in the state space by simulated random measures called particles. However, particle filters lack efficient search strategies. In this paper, we propose a general framework to hybridize heuristics=metaheuristics with particle filters properly. The aim of this framework is to devise effective hybrid visual tracking algorithms naturally, guided by the use of abstraction techniques. Resulting algorithms exploit the benefits of both complementary approaches. As a particular example, a memetic algorithm particle filter is derived from the proposed hybridization framework. Finally, we show the performance of the memetic algorithm particle filter when it is applied to a multiple object tracking problem.

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