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

Sonia Mota, Eduardo Ros, Francisco de Toro

Abstract


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.

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