49 resultados para Street lighting.
Resumo:
In this paper the authors propose a new technique for determining a confidence factor applied to the performance prediction of individual luminaires within an overall pattern of luminaires. This work has relevance to any application where it is necessary to determine the performance of a lighting pattern e.g. street lighting, signal lighting etc. In this paper we apply our technique to a transportation application, namely, an airport landing lighting pattern. In the aviation industry it is imperative that the landing lighting pattern at individual airports performs according to standards. We have developed an automated technique which can be used to access the performance of luminaires within this pattern. We extend this work to also derive a confidence factor related to this prediction based on the quality of the data being utilised. ©2010 IEEE.
Resumo:
The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting. The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade–Lucus–Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.