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Traffic accidents have become an important cause of fatality in modern countries. For instance, in 2002, motor vehicle accidents represented the half of non-natural death in the United States (National Center of Health Statistics, 2002); while in 2003 there were reported almost 150,000 injured and 7,000 killed in pedestrian accidents only in the European Union (United Nations Economic Commission for Europe, 2005). In order to improve safety, the industry has progressively developed different elements of increasing complexity and performance: from turn signals and seat belts to Anti-lock Braking Systems (ABS) and internal Airbags. Recently, research has moved towards even more intelligent on-board systems that aim to anticipate and prevent the accidents, or at least, minimize their effects when unavoidable. They are referred as Advanced Driver Assistance Systems (ADAS), in the sense that they assist the driver to take decisions, provide warnings in dangerous driving situations, and even at taking automatic evasive actions in extreme cases. One of the most prominent components of ADAS are the vision systems (monocular or stereo), which capture in a single snapshot all the surrounding information. Although monocular vision systems allow higher acquisition/processing rates, the use of on-board stereo vision heads is gaining popularity in ADAS applications. Stereo rigs are able to provide 3D information useful for facing up problems that can not be tackled with monocular systems (e.g., reliable distance estimation). Furthermore, the current technology is producing more and more inexpensive and compact stereo vision systems that let us think on a promising future. Accurate and real time camera pose estimation is one of the common difficulties of on-board vision systems. Applications such as obstacle avoidance, pedestrian detection, or traffic signal recognition, could both speed up the whole process and make use of additional information by a precise estimation of the current camera extrinsic parameters, related to the

Stereo Vision Camera Pose Estimation for On-Board Applications