Computer Stereo Vision for Autonomous Driving: Theory and Algorithms: Theory and Algorithms

Rui Fan*, Li Wang, Mohammud Junaid Bocus, Ioannis Pitas

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

6 Citations (Scopus)
3 Downloads (Pure)

Abstract

As an important component of autonomous systems, autonomous car perception has had a big leap with recent advances in parallel computing architectures. With the use of tiny but full-feature embedded supercomputers, computer stereo vision has been prevalently applied in autonomous cars for depth perception. The two key aspects of computer stereo vision are speed and accuracy. They are both desirable but conflicting properties, as the algorithms with better disparity accuracy usually have higher computational complexity. Therefore, the main aim of developing a computer stereo vision algorithm for resource-limited hardware is to improve the trade-off between speed and accuracy. In this chapter, we introduce both the hardware and software aspects of computer stereo vision for autonomous car systems. Then, we discuss four autonomous car perception tasks, including 1) visual feature detection, description and matching, 2) 3D information acquisition, 3) object detection/recognition and 4) semantic image segmentation. The principles of computer stereo vision and parallel computing on multi-threading CPU and GPU architectures are then detailed.
Original languageEnglish
Title of host publicationRecent Advances in Computer Vision Applications Using Parallel Processing
Subtitle of host publicationRecent Advances in Computer Vision Applications Using Parallel Processing
EditorsKhalid M. Hosny, Ahmad Salah
PublisherSpringer, Cham
Pages41-70
Number of pages30
Volume1073
ISBN (Electronic)978-3-031-18735-3
ISBN (Print)978-3-031-18734-6
DOIs
Publication statusPublished - 24 Jan 2023

Publication series

NameStudies in Computational Intelligence
Volume1073
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Bibliographical note

Funding Information:
Acknowledgements This work was supported by the National Key R&D Program of China, under grant No. 2020AAA0108100, awarded to Prof. Qijun Chen. This work was also supported by the Fundamental Research Funds for the Central Universities, under projects No. 22120220184, No. 22120220214, and No. 2022-5-YB-08, awarded to Prof. Rui Fan. This work has also received partial funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871479 (AERIALCORE).

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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