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