Detecting hazards based on disparity maps using machine learning for autonomous machine systems and applications

Abstract

In various examples, systems and methods for machine learning based hazard detection for autonomous machine applications using stereo disparity are presented. Disparity between a stereo pair of images is used to generate a path disparity model. Using the path disparity model, a machine learning model can recognize when a pixel in the first image corresponds to a pixel in the second image even though the pixel in the two images does not have identical characteristics. Similarities in extracted feature vectors can be computed and represented by a vector similarity metric that is input to a machine learning classifier, along with feature information extracted from the stereo image pair, to differentiate hazard pixels from non-hazard pixels. In some embodiments, a V-space disparity map, where a first axis corresponds to disparity values and the second axis corresponds to pixel rows, may be used to simplify estimation of the path disparity model.