Abstract-This paper presents a system whose aim is to detect and classify road obstacles, like pedestrians and vehicles, by fusing data coming from different sensors: a camera, a radar, and an inertial sensor. The camera is mainly used to refine the vehicles' boundaries detected by the radar and to discard those who might be false positives; at the same time, a symmetry based pedestrian detection algorithm is executed, and its results are merged with a set of regions of interest, provided by a Motion Stereo technique.The tests have been performed in several environments and traffic situations, their results showed how the vision based filtering provides an effective reduction of radar's false positives; furthermore, the regions of interest detected by the Motion Stereo algorithm, truly improves the pedestrian detector's performance again by keeping low the number of detection errors.The system has been shown during the APALACI-PReVENT European IP final demonstration 1 in September 2007 in Versailles (France).

No abstract

No abstract

Abstract-The performance of many computer vision applications, especially in the automotive field, can be greatly increased if camera oscillations induced by movements of the acquisition devices are corrected by a stabilization system. An effective stabilizer should cope with different oscillation frequencies and amplitude intervals, and work in a wide range of environments (such as urban, extra-urban or even unstructured ones). In this paper we will analyze three different approaches, based on signature, feature, and correlation tracking respectively, that have been devised to face these problems.

No abstract