  Cameras are the primary sensor in automated driving systems. They provide
high information density and are optimal for detecting road infrastructure cues
laid out for human vision. Surround view cameras typically comprise of four
fisheye cameras with 190{\deg} field-of-view covering the entire 360{\deg}
around the vehicle focused on near field sensing. They are the principal sensor
for low-speed, high accuracy and close-range sensing applications, such as
automated parking, traffic jam assistance and low-speed emergency braking. In
this work, we describe our visual perception architecture on surround view
cameras designed for a system deployed in commercial vehicles, provide a
functional review of the different stages of such a computer vision system, and
discuss some of the current technological challenges. We have designed our
system 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 how they are
synergized to form a complete system. Qualitative results are presented in the
video at \url{https://youtu.be/ae8bCOF77uY}.
