Systematic benchmarking for reproducibility of computer vision algorithms for real-time systems: The example of optic flow estimation
Paper in proceeding, 2019

Until now there have been few formalized methods for conducting systematic benchmarking aiming at reproducible results when it comes to computer vision algorithms. This is evident from lists of algorithms submitted to prominent datasets, authors of a novel method in many cases primarily state the performance of their algorithms in relation to a shallow description of the hardware system where it was evaluated. There are significant problems linked to this non-systematic approach of reporting performance, especially when comparing different approaches and when it comes to the reproducibility of claimed results. Furthermore how to conduct retrospective performance analysis such as an algorithm's suitability for embedded real-time systems over time with underlying hardware and software changes in place. This paper proposes and demonstrates a systematic way of addressing such challenges by adopting containerization of software aiming at formalization and reproducibility of benchmarks. Our results show maintainers of broadly accepted datasets in the computer vision community to strive for systematic comparison and reproducibility of submissions to increase the value and adoption of computer vision algorithms in the future.




embedded systems

real-time computing

computer vision



optic flow



Björnborg Nguyen

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Engineering and Autonomous Systems

Christian Berger

University of Gothenburg

Ola Benderius

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Engineering and Autonomous Systems

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2153-0858 (ISSN) 2153-0866 (eISSN)

9781728140049 (ISBN)

International Conference on Intelligent Robots and Systems (IROS)
Macau, China,

Subject Categories

Computer Engineering

Software Engineering

Computer Science

Computer Systems

Computer Vision and Robotics (Autonomous Systems)

Areas of Advance

Information and Communication Technology

Driving Forces

Innovation and entrepreneurship


ReVeRe (Research Vehicle Resource)



More information

Latest update