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This paper describes a method for the interpretation of traffic scenes based on the detection and recognition of those objects, or classes of objects which are typically found in an urban scene. The guideline of the approach is the following. Since generic model-based recognition schemes are unsuitable for the analysis of traffic scenes and result in very poor performances, each of the different classes of objects which we expect t o find in a typical scene is identified according t o some selected features. After identifying the object, its main parameters are computed and. when needed, the object is furt,her classified The classes of objects we have considered included the roadbed, vehicles, buildings, trees, crosswalks, road signs.The method described here has been successfully tested on a wide set of images of traffic scenes and provided a general-purpose reconstruction of the whole traffic scene as viewed by the driver.

This paper describes a method for detecting and recognizing road signs in grey-level images acquired by a single camera mounted on a moving vehicle.An extensive experimentation has shown that the method is robust against low-level noise corrupting edge detection and contour following, and works for images of cluttered urban streets as well as country roads and highways. A further improvement on the detection and recognition scheme has been obtained by means of a Kalman-filter-based temporal integration of the extracted information. The proposed approach can be very helpful for the development of a system for driving assistance.