Due to different visual tasks and gaze patterns, the discomfort glare experienced by pedestrians may differ from that experienced by drivers. This paper investigates the discomfort glare experienced by pedestrians under various urban LED luminaires through psychovisual experiments conducted on a test track. The ability of state-of-the-art models to predict the level of discomfort glare, measured on the de Boer rating scale, for this application is also investigated. With one exception, the models all overestimate the mean subjective discomfort glare compared to the experimental data. Models proposed by Lin et al (2015) and Bullough et al (2008Bullough et al ( , 2011 perform well. However, the implementation of these models is not straightforward because choices are needed to estimate some of the variables such as the background luminance and the glare source area.

Atmospheric visibility is an important input for road and air transportation safety, as well as a good proxy to estimate the air quality. A model-driven approach is presented to monitor the meteorological visibility distance through use of ordinary outdoor cameras. Unlike in previous data-driven approaches, a physics-based model is proposed which describes the mapping function between the contrast in the image and the atmospheric visibility. The model is non-linear, which allows encompassing a large spectrum of applications. The model assumes a continuous distribution of objects with respect to the distance in the scene and is estimated by a novel process. It is more robust to illumination variations by selecting the Lambertian surfaces in the scene. To evaluate the relevance of the approach, a publicly available database is used. When the model is fitted to short range data, the proposed method is shown to be effective and to improve on existing methods. In particular, it allows envisioning an easier deployment of these camera-based techniques on multiple observation sites.

The AWARE (All Weather All Roads Enhanced vision) French public funded project is aiming at the development of a low cost sensor fitting to automotive and aviation requirements, and enabling a vision in all poor visibility conditions, such as night, fog, rain and snow.In order to identify the technologies providing the best all-weather vision, we evaluated the relevance of four different spectral bands: Visible RGB, Near-Infrared (NIR), Short-Wave Infrared (SWIR) and Long-Wave Infrared (LWIR). Two test campaigns have been realized in outdoor natural conditions and in artificial fog tunnel, with four cameras recording simultaneously.This paper presents the detailed results of this comparative study, focusing on pedestrians, vehicles, traffic signs and lanes detection.

Abstract-This paper proposes an improvement of Advanced Driver Assistance System based on saliency estimation of road signs. After a road sign detection stage, its saliency is estimated using a SVM learning. A model of visual saliency linking the size of an object and a size-independent saliency is proposed. An eye tracking experiment in context close to driving proves that this computational evaluation of the saliency fits well with human perception, and demonstrates the applicability of the proposed estimator for improved ADAS.

Abstract. Estimating the atmospheric or meteorological visibility distance is very important for air and ground transport safety, as well as for air quality. However, there is no holistic approach to tackle the problem by camera. Most existing methods are data-driven approaches which perform a linear regression between the contrast in the scene and the visual range estimated by means of reference additional sensors. In this paper, we propose a probabilistic model-based approach which takes into account the distribution of contrasts in the scene. It is robust to illumination variations in the scene by taking into account the Lambertian surfaces. To evaluate our model, meteorological ground truth data were collected, showing very promising results. This works opens new perspectives in the computer vision community dealing with environmental issues.