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Automated Parking is a low speed manoeuvring scenario which is quiteunstructured and complex, requiring full 360{\deg} near-field sensing aroundthe vehicle. In this paper, we discuss the design and implementation of anautomated parking system from the perspective of camera based deep learningalgorithms. We provide a holistic overview of an industrial system covering theembedded system, use cases and the deep learning architecture. We demonstrate areal-time multi-task deep learning network called FisheyeMultiNet, whichdetects all the necessary objects for parking on a low-power embedded system.FisheyeMultiNet runs at 15 fps for 4 cameras and it has three tasks namelyobject detection, semantic segmentation and soiling detection. To encouragefurther research, we release a partial dataset of 5,000 images containingsemantic segmentation and bounding box detection ground truth via WoodScapeproject \cite{yogamani2019woodscape}.

FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System