Système de vision embarqué pour la détection d’obstacles routiers

Lucas, Yves; Domingues, Antonio; Boubal, Michel; Marché, P.

doi:10.51257/a-v1-in24

Véhicule Et Mobilité Du Futur 2004

DOI: 10.51257/a-v1-in24

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Système de vision embarqué pour la détection d’obstacles routiers

Yves Lucas¹,

Antonio Domingues²,

Michel Boubal³

et al.

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Modeling, Evaluation and Control of a Road Image Processing Chain

et al. 2005

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Tuning a complete image processing chain (IPC) remains a tricky step. Until now researchers focused on the evaluation of single algorithms, based on a small number of test images and ad hoc tuning independent of input data. In this paper we explain how, by combining statistical modeling with design of experiments, numerical optimization and neural learning, it is possible to elaborate a powerful and adaptive IPC. To succeed, it is necessary to build a large image database, to describe input images and finally to evaluate the IPC output. By testing this approach on an IPC dedicated to road obstacle detection, we demonstrate that this experimental methodology and software architecture ensure a steady efficiency. The reason is simple: the IPC is globally optimized, from a large number of real images (180 out of a sequence of 30 000) and with adaptive processing of input data

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Modeling, Evaluation and Control of a Road Image Processing Chain

et al. 2005

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Système de vision embarqué pour la détection d’obstacles routiers

Cited by 1 publication

References 0 publications

Modeling, Evaluation and Control of a Road Image Processing Chain

Modeling, Evaluation and Control of a Road Image Processing Chain

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