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Cameras are the primary sensor in automated driving systems. They providehigh information density and are optimal for detecting road infrastructure cueslaid out for human vision. Surround-view camera systems typically comprise offour fisheye cameras with 190{\deg}+ field of view covering the entire360{\deg} around the vehicle focused on near-field sensing. They are theprincipal sensors for low-speed, high accuracy, and close-range sensingapplications, such as automated parking, traffic jam assistance, and low-speedemergency braking. In this work, we provide a detailed survey of such visionsystems, setting up the survey in the context of an architecture that can bedecomposed into four modular components namely Recognition, Reconstruction,Relocalization, and Reorganization. We jointly call this the 4R Architecture.We discuss how each component accomplishes a specific aspect and provide apositional argument that they can be synergized to form a complete perceptionsystem for low-speed automation. We support this argument by presenting resultsfrom previous works and by presenting architecture proposals for such a system.Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.

Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras