LAUSR

Fish-eye cameras have become essential sensors in intelligent vehicles. Due to its unique projection principle, a fish-eye camera can provide a large field of view. Benefiting from this special feature, fish-eye cameras have rich applications in intelligent vehicles. However, dataset and distortion problems are still challenges when applying fish-eye cameras in reality. This work introduces the projection principle of fish-eye cameras, and four classic fish-eye image representation models are presented. Then, the typical fish-eye datasets are presented, including real collected data and virtually generated data. Through the organization and summarization of the relevant studies, we demonstrate various applications of fish-eye cameras in intelligent vehicles, e.g., object detection and tracking, image segmentation, mapping and localization, and around-view monitoring. These works design various strategies to exploit the advantages of fish-eye cameras and prevent image distortion problems, showing the broad application prospects of such cameras. Finally, we discuss the development tendencies of intelligent vehicle applications involving fish-eye cameras.