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

Abstract

In examples, system and methods for stereo disparity based hazard detection for autonomous machine applications are presented. Example embodiments may assist an ego-machine in detecting hazards within its path of travel. The systems and methods may use disparity between a stereo pair of images to generate a baseline path disparity model and further identify hazards from detected disparities that deviate from that path disparity model. A disparity map for the image pair is constructed in which each pixel represents a disparity for a corresponding element of the image captured. Blockwise division may be optionally used to subdivide the disparity map into a plurality of smaller disparity maps, each corresponding to a block of pixels of the disparity map. 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.