Analyzing Computer Vision Data - The Good, the Bad and the Ugly

In recent years, a great number of datasets were published to train and evaluate computer vision (CV) algorithms. These valuable contributions helped to push CV solutions to a level where they can be used for safetyrelevant applications, such as autonomous driving. However, major questions concerning quality and usefulness of test data for CV evaluation are still unanswered. Researchers and engineers try to cover all test cases by using as much test data as possible. In this paper, we propose a different solution for this challenge. We introduce a method for dataset analysis which builds upon an improved version of the CV-HAZOP checklist, a list of potential hazards within the CV domain. Picking stereo vision as an example, we provide an extensive survey of 28 datasets covering the last two decades. We create a tailored checklist and apply it to the datasets Middlebury, KITTI, Sintel, Freiburg, and HCI to present a thorough characterization and quantitative comparison. We confirm the usability of our checklist for identification of challenging stereo situations by applying nine state-of-theart stereo matching algorithms on the analyzed datasets, showing that hazard frames correlate with difficult frames. We show that challenging datasets still allow a meaningful algorithm evaluation even for small subsets. Finally, we provide a list of missing test cases that are still not covered by current datasets as inspiration for researchers who want to participate in future dataset creation.