A computing architecture for correcting perspective distortion in motion-detection based visual systems

The projection of 3D scenarios onto 2D surfaces produces distortion on the resulting images that affects the accuracy of low-level motion primitives. Independently of the motion detection algorithm used, post-processing stages that use motion data are dominated by this distortion artefact. Therefore we need to devise a way of reducing the distortion effect in order to improve the post-processing capabilities of a vision system based on motion cues. In this paper we adopt a space-variant mapping strategy, and describe a computing architecture that finely pipelines all the processing operations to achieve high performance reliable processing. We validate the computing architecture in the framework of a real-world application, a vision-based system for assisting overtaking manoeuvres using motion information to segment approaching vehicles. The overtaking scene from the rear-view mirror is distorted due to perspective, therefore a space-variant mapping strategy to correct perspective distortion arterfaces becomes of high interest to arrive at reliable motion cues.