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One of the most relevant tasks in an intelligent vehicle navigation system isthe detection of obstacles. It is important that a visual perception system fornavigation purposes identifies obstacles, and it is also important that thissystem can extract essential information that may influence the vehicle'sbehavior, whether it will be generating an alert for a human driver or guide anautonomous vehicle in order to be able to make its driving decisions. In thispaper we present an approach for the identification of obstacles and extractionof class, position, depth and motion information from these objects thatemploys data gained exclusively from passive vision. We performed ourexperiments on two different data-sets and the results obtained shown a goodefficacy from the use of depth and motion patterns to assess the obstacles'potential threat status.

Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data