ROAD MARKING EXTRACTION USING A MODEL&DATA-DRIVEN RJ-MCMC

We propose an integrated bottom-up/top-down approach to road-marking extraction from image space. It is based on energy minimization using marked point processes. A generic road marking object model enable us to define universal energy functions that handle various types of road-marking objects (das...

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Bibliographic Details
Main Authors: Hervieu, A., Soheilian, B., Brédif, M.
Format: Conference Proceeding
Language:English
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Summary:We propose an integrated bottom-up/top-down approach to road-marking extraction from image space. It is based on energy minimization using marked point processes. A generic road marking object model enable us to define universal energy functions that handle various types of road-marking objects (dashed-lines, arrows, characters, etc.). A RJ-MCMC sampler coupled with a simulated annealing is applied to find the configuration corresponding to the minimum of the proposed energy. We used input data measurements to guide the sampler process (data driven RJ-MCMC). The approach is enhanced with a model-driven kernel using preprocessed autocorrelation and inter-correlation of road-marking templates, in order to resolve type and transformation ambiguities. The method is generic and can be applied to detect road-markings in any orthogonal view produced from optical sensors or laser scanners from aerial or terrestrial platforms. We show the results an ortho-image computed from ground-based laser scanning.
ISSN:2194-9050
2194-9042
2194-9050
DOI:10.5194/isprsannals-II-3-W4-47-2015