Abstract-One source of accidents when driving a vehicle is the presence of fog. Fog fades the colors and reduces the contrasts in the scene with respect to their distances from the driver. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement in road images. The visibility enhancement algorithm proposed in [1] is not optimized for road images. In this paper, we reformulate the problem as the inference of the local atmospheric veil from constraints. The algorithm in [1] thus becomes a particular case. From this new derivation, we propose to better handle road images by introducing an extra constraint taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are the speed, the possibility to handle both color and gray-level images, and the small number of parameters. A new scheme is proposed for rating visibility enhancement algorithms based on the addition of several types of generated fog on synthetic and camera images. A comparative study and quantitative evaluation with other state-of-the-art algorithms is thus proposed. This evaluation demonstrates that the new algorithm produces better results with homogeneous fog and that it is able to deal better with the presence of heterogeneous fog. Finally, we also propose a model allowing to evaluate the potential safety benefit of an ADAS based on the display of defogged images.
Free space detection is a primary task for car navigation. Unfortunately, classical approaches have difficulties in adverse weather conditions, in particular in daytime fog. In this paper, a solution is proposed thanks to a contrast restoration approach on images grabbed by an in-vehicle camera. The proposed method improves the state of the art in several ways. First, the segmentation of the fog region of interest is better segmented thanks to the computation of shortest routes maps. Second, the fog density as well as the position of the horizon line are jointly computed. Then, the method restores the contrast of the road by only assuming that the road is flat and, at the same time, detects the vertical objects. Finally, a segmentation of the connected component in front of the vehicle gives the free space area. An experimental validation was carried out to foresee the effectiveness of the method. Different results are shown on sample images extracted from video sequences acquired from an in-vehicle camera. The proposed method is complementary to existing free space area detection methods relying on color segmentation and stereovision.
Road accidents because of fog are relatively rare but their severity is greater and the risk of pile-up is higher. However, processing the images grabbed by cameras embedded in the vehicles can restore some visibility. Tarel et al. (2012) proposed to implement head up displays (HUD) to help drivers anticipate potential collisions by displaying dehazed images of the road scene. In the present study, three experiments have been designed to quantify the expected gain of such a system in terms of the driver's reaction time (RT). The first experiment compares the RT with and without dehazing, giving quantitative evidence that such an advanced driving assistance system (ADAS) may improve road safety. Then, based on a modified Piéron's law, a quantitative model is proposed, linking the RT to the target visibility (V t ), which can be computed from onboard camera images. Two additional experiments have been conducted, giving evidence that the proposed RT model, computed from V t , is robust with respect to contextual cues, to contrast polarity and to population sample. The authors finally propose to use this predictive model to switch on/off the proposed HUD-based ADAS.
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