Contributions to the Information Fusion . Application to Obstacle Recognition in Visible and Infrared Images

The interest for the intelligent vehicle field has been increased during the last years, must probably due to an important number of road accidents. Many accidents could be avoided if a device attached to the vehicle would assist the driver with some warnings when dangerous situations are about to appear. In recent years, leading car developers have recorded significant efforts and support research works regarding the intelligent vehicle field where they propose solutions for the existing problems, especially in the vision domain. Road detection and following, pedestrian or vehicle detection, recognition and tracking, night vision, among others are examples of applications which have been developed and improved recently. Still, a lot of challenges and unsolved problems remain in the intelligent vehicle domain. Our purpose in this thesis is to design an Obstacle Recognition system for improving the road security by directing the driver’s attention towards situations which may become dangerous. Many systems still encounter problems at the detection step and since this task is still a work in progress in the frame of the LITIS laboratory (from INSA), our goal was to develop a system to continue and improve the detection task. We have focused solely on the fusion between the visible and infrared fields from the viewpoint of an Obstacle Recognition module. Our main purpose was to investigate if the combination of the visible-infrared information is efficient, especially if it is associated with an SVM (Support Vector Machine)-based classification. The outdoor environment, the variety of obstacles appearance from the road scene (considering also the multitude of possible types of obstacles), the cluttered background and the fact that the system must cope with the moving vehicle constraints make the categorization of road obstacles a real challenge. In addition, there are some critical requirements that a driver assistance system should fulfil in order to be considered a possible solution to be implemented on board of a vehicle: the system cost should be low enough to allow to be incorporated in every series vehicle, the system has to be fast enough to detect and then recognize obstacles in real time, it has to be efficient (to detect all obstacles with very few false alarms) and robust (to be able to face different difficult environmental conditions). To outline the system, we were looking for sensors which could provide enough information to detect obstacles (even those occluded) in any illumination or weather situation, to recognize them and to identify their position in the scene. In the intelligent vehicle domain there is no such a perfect sensor to handle all these concerned tasks, but there are systems employing one or many different sensors in order to perform obstacles detection, recognition or tracking or some combination of them. After comparing advantages and disadvantages between passive and active technologies, we chose the proper sensors for developing our Obstacle Detection and Recognition system. Due to possible interferences among active sensors, which could be critical for a large number of vehicles moving simultaneously in the same environment, we concentrate on using passive sensors, which are non-invasive, like cameras. Therefore, our proposed system employ visible spectrum and infrared spectrum cameras, which are relatively chosen to be complementary, because the system must work well even under difficult conditions, like poor illumination or bad-weather situations (such as dark, rain, fog). The monomodal systems are adapted to a single modality, either visible or infrared and even if they provide good recognition rates on the test set, these results could be improved by the combined processing of the visible and infrared information, which means in the frame of a bimodal system. The bimodal systems could take different forms in function of the level at which the information is combined or fused. Thus, we propose three different fusion systems: at the levels of features or at the te l-0 06 21 20 2, v er si on 1 9 Se p 20 11