2019/12/23 by Pullarao Maddu, Wayne Doherty, Maddu, Pullarao +17
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics (cs.RO) #Smart Parking Systems Research #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.1912.11066
openalex created_date 2019/09/26 · openalex publication_date 2019/12/23 · openalex updated_date 2026/07/28
Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360° near-field sensing around the vehicle. In this paper, we discuss the design and implementation of an automated parking system from the perspective of camera based deep learning algorithms. We provide a holistic overview of an industrial system covering the embedded system, use cases and the deep learning architecture. We demonstrate a real-time multi-task deep learning network called FisheyeMultiNet, which detects 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 namely object detection, semantic segmentation and soiling detection. To encourage further research, we release a partial dataset of 5,000 images containing semantic segmentation and bounding box detection ground truth via WoodScape project \citeyogamani2019woodscape.