A Model for Automatic Diagnostic of Road Signs Saliency

Road signs, the main communication media towards the drivers, play a significant role in road safety and traffic control through drivers‟ guidance, warning, and information. However, not all traffic signs are seen by all drivers, which sometimes lead to dangerous situations. In order to manage safer roads, the estimation of the legibility of the road environment is thus of importance for road engineers and authorities who aim at making and keeping traffic signs salient enough to attract attention regardless of the driver's workload. Our long term objective is to build a system for the automatic estimation of road sign saliency along a road network, from images taken with a digital camera on-board a vehicle. This system will be interesting for accident analysis and prevention since it will enable a fine diagnostic of the road signs saliency, helping the road manager decide on which signs he must act and how (replacement or background modification). This should lead to improved asset management, road infrastructure maintenance and road safety. What attracts driver's attention is related both to psychological factors (motivations, driving task, etc.) and to the photometrical and geometrical characteristics of the road scene (colours, background, etc.). The saliency (or conspicuity) of an object is the degree to which this object attracts visual attention for a given background. Road signs perception depends on the two main components of visual attention: objects pop-out and visual search. The first one is less relevant when the task is to search for a particular object, whereas one important part of the driving task is to look for road signs. As most of current computational models of visual search saliency are limited to laboratorysituations, we propose a new model to compute visual search saliency in natural scenes. Relying on statistical learning algorithms, the proposed algorithm emulates the priors a driver learns on object appearance for any given class of road signs. The algorithm performs both the detection of the object of interest in the image and the estimation of its saliency. The proposed computational model of saliency was evaluated through psycho-visual experiments. This opens the possibility to design automatic diagnostic systems for road signs saliency.