Obstacle Detection Quality as a Problem-Oriented Approach to Stereo Vision Algorithms Estimation in Road Situation Analysis

TL;DR

This paper introduces a problem-oriented evaluation method for stereo vision obstacle detection in road scenarios, aiming to improve performance assessment in autonomous driving systems.

Contribution

It proposes a new performance evaluation approach tailored to road obstacle detection, reducing dataset preparation effort and serving as a quality criterion for stereo vision algorithms.

Findings

Efficient evaluation method tailored for road obstacle detection

Applicable to self-driving cars and driver assistance systems

Enhances stereo vision algorithm assessment

Abstract

In this work we present a method for performance evaluation of stereo vision based obstacle detection techniques that takes into account the specifics of road situation analysis to minimize the effort required to prepare a test dataset. This approach has been designed to be implemented in systems such as self-driving cars or driver assistance and can also be used as problem-oriented quality criterion for evaluation of stereo vision algorithms.