2000 character limit reached
Measuring Natural Scenes SFR of Automotive Fisheye Cameras (2401.05232v1)
Published 10 Jan 2024 in cs.CV
Abstract: The Modulation Transfer Function (MTF) is an important image quality metric typically used in the automotive domain. However, despite the fact that optical quality has an impact on the performance of computer vision in vehicle automation, for many public datasets, this metric is unknown. Additionally, wide field-of-view (FOV) cameras have become increasingly popular, particularly for low-speed vehicle automation applications. To investigate image quality in datasets, this paper proposes an adaptation of the Natural Scenes Spatial Frequency Response (NS-SFR) algorithm to suit cameras with a wide field-of-view.
- V. R. Kumar, C. Eising, C. Witt, and S. Yogamani, “Surround-view fisheye camera perception for automated driving: Overview, survey & challenges,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 4, pp. 3638–3659, 2023.
- C. Eising, J. Horgan, and S. Yogamani, “Near-field perception for low-speed vehicle automation using surround-view fisheye cameras,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 13 976–13 993, 2022.
- A. Eichenseer and A. Kaup, “A data set providing synthetic and real-world fisheye video sequences,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 1541–1545.
- P. Müller and A. Braun, “Simulating optical properties to access novel metrological parameter ranges and the impact of different model approximations,” in 2022 IEEE International Workshop on Metrology for Automotive, 2022, pp. 133–138.
- P. D. Burns and D. Williams, “Camera resolution and distortion: Advanced edge fitting,” Electronic Imaging, vol. 30, pp. 1–5, 2018.
- O. Van Zwanenberg, S. Triantaphillidou, and R. Jenkin, “A tool for deriving camera spatial frequency response from natural scenes (ns-sfr),” Electronic Imaging 2023: Image Quality & System Performance XX, 2023.
- O. van Zwanenberg, “Camera spatial frequency response derived from pictorial natural scenes,” Ph.D. dissertation, University of Westminster, 2022.
- O. Van Zwanenberg, S. Triantaphillidou, R. Jenkin, and A. Psarrou, “Natural scene derived camera edge spatial frequency response for autonomous vision systems,” in IS&T/IoP London Imaging Meeting, 2021.
- O. van Zwanenberg, S. Triantaphillidou, R. B. Jenkin, and A. Psarrou, “Estimation of iso12233 edge spatial frequency response from natural scene derived step-edge data,” Journal of Imaging Science and Technology, vol. 65, no. 6, pp. 60 402–1, 2018.
- O. van Zwanenberg, S. Triantaphillidou, R. Jenkin, and A. Psarrou, “Edge detection techniques for quantifying spatial imaging system performance and image quality,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.
- A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The KITTI dataset,” The International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013.
- A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Conference on robot learning, 2017, pp. 1–16.
- P. D. Burns, D. Williams, J. Griffith, H. Hall, and S. Cahall, “Application of iso standard methods to optical design for image capture,” Electronic Imaging, vol. 2020, no. 9, pp. 240–1, 2020.
- C. Hogan and G. Sistu, “Automatic vehicle ego body extraction for reducing false detections in automated driving applications,” in Irish Conference on Artificial Intelligence and Cognitive Science. Springer, 2022, pp. 264–275.