Do we need a standardized taxonomy behind the image training data for self-driving vehicles? - iMerit

This post is by Emanuel Ott, a Solutions Architect at iMerit and an expert in machine learning and computer vision. It summarizes a talk given at the Machine Intelligence in Autonomous Vehicles Summit in Amsterdam. To create an algorithm that learns to'see' a typical road the way humans do, data experts first need to classify and then label the different components of the road: for example, "this is a tree, this is another car, this is the curb of the road". A process which is natural to the human eye and brain needs to be entirely dissected in order to build the data that feeds the algorithm that powers image recognition for a self-driving car. This is not without challenges, the chief one for data experts being: how do I'tell what I see' in a particular image of a road, in words that are predefined and common to all the data experts working on one data set?