  The ability to detect pedestrians and other moving objects is crucial for an
autonomous vehicle. This must be done in real-time with minimum system
overhead. This paper discusses the implementation of a surround view system to
identify moving as well as static objects that are close to the ego vehicle.
The algorithm works on 4 views captured by fisheye cameras which are merged
into a single frame. The moving object detection and tracking solution uses
minimal system overhead to isolate regions of interest (ROIs) containing moving
objects. These ROIs are then analyzed using a deep neural network (DNN) to
categorize the moving object. With deployment and testing on a real car in
urban environments, we have demonstrated the practical feasibility of the
solution. The video demos of our algorithm have been uploaded to Youtube:
https://youtu.be/vpoCfC724iA, https://youtu.be/2X4aqH2bMBs
