Survey on Fish-Eye Cameras and Their Applications in Intelligent Vehicles

Qian, Yeqiang; Yang, Ming; Dolan, John M.

doi:10.1109/tits.2022.3210409

Cited by 6 publications

(4 citation statements)

References 114 publications

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“…fisheye projection models, the surround-view camera system, perception tasks, and popular automotive datasets) is conducted. In [17], a superficially similar survey to [1] is provided. However, in [17], the authors provide more detail on automotive fisheye datasets (both real and simulated), and the three tasks of object detection and tracking, semantic segmentation, mapping and localization, and surround-view monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…In [17], a superficially similar survey to [1] is provided. However, in [17], the authors provide more detail on automotive fisheye datasets (both real and simulated), and the three tasks of object detection and tracking, semantic segmentation, mapping and localization, and surround-view monitoring. Other fisheye-related surveys such as Gao et al [18], focus on the different types of wide field-of-view (FOV) systems for 360 • panoramic, catadioptric as well as briefly discussing fisheye segmentation for autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

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Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges

Jakab,

Deegan,

Sharma

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving and ADAS. The automotive industry has advanced in applying state-of-the-art computer vision to enhance road safety and provide automated driving functionality. When using camera systems on vehicles, there is a particular need for a wide field of view to capture the entire vehicle's surroundings, in areas such as low-speed maneuvering, automated parking, and cocoon sensing. However, one crucial challenge in surround-view cameras is the strong optical aberrations of the fisheye camera, which is an area that has received little attention in the literature. Additionally, a comprehensive dataset is needed for testing safety-critical scenarios in vehicle automation. The industry has turned to simulation as a cost-effective strategy for creating synthetic datasets with surround-view camera imagery. We examine different simulation methods (such as model-driven and data-driven simulations) and discuss the simulators' ability (or lack thereof) to model real-world optical performance. Overall, this paper highlights the optical aberrations in automotive fisheye datasets, and the limitations of optical reality in simulated fisheye datasets, with a focus on computer vision in surround-view optical systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges

Jakab,

Deegan,

Sharma

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…Fisheye cameras are utilized in fields such as robotics and autonomous driving due to their wide Field of View (FoV), which provides more environmental information than pinhole cameras [1]. They are particularly used in technologies such as Visual Odometry and Simultaneous Localization And Mapping (SLAM) for estimating mobile motion ( [2], [3], [4]).…”

Section: Introductionmentioning

confidence: 99%

Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons

Lee

et al. 2020

Sensors

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3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundaries for positioning and heading estimation. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude, longitude coordinate and heading angle to determine the precise location of the fisheye image. The results are then compared with the smartphone GNSS and advanced 3DMA GNSS positioning methods. The proposed method provides degree-level heading accuracy, and improved positioning accuracy similar to other advanced 3DMA GNSS positioning methods in a rich urban environment.

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“…To illustrate, [10] and [11] review the dataset and methods available for semantic segmentation and object detection for better perception. In [16], [17], the advancements in panoramic imaging for better scene understanding and applications of fish-eye cameras in the ADS domain are reviewed. Introspection, however, is not reviewed as thoroughly as other safety approaches in the literature and is fragmented under different titles and fields.…”

mentioning

confidence: 99%

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Yatbaz,

Dianati,

Woodman

2024

IEEE Trans. Intell. Transport. Syst.

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Survey on Fish-Eye Cameras and Their Applications in Intelligent Vehicles

Cited by 6 publications

References 114 publications

Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges

Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges

Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

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