In recent years advanced driver assistance systems (ADAS) have received increasing interest to confront car accidents.
In particular, video processing based vehicle detection methods are emerging as an efficient way to address accident
prevention. Many video-based approaches are proposed in the literature for vehicle detection, involving sophisticated
and costly computer vision techniques. Most of these methods require ad hoc hardware implementations to attain
real-time operation. Alternatively, other approaches perform a domain change --via transforms like FFT, inverse
perspective mapping (IPM) or Hough transform-- that simplifies otherwise complex feature detection. In this work, a
cooperative strategy between two domains, the original perspective space and the transformed non-perspective space
computed trough IPM, is proposed in order to alleviate the processing load in each domain by maximizing the
information exchange between the two domains. A system is designed upon this framework that computes the location
and dimension of the vehicles in a video sequence. Additionally, the system is made scalable to the complexity imposed
by the scenario. As a result, real-time vehicle detection and tracking is accomplished in a general purpose platform. The
system has been tested for sequences comprising a wide variety of scenarios, showing robust and accurate performance.