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Fisheyemodnet: Moving Object Detection On Surround-View Cameras For Autonomous Driving, Marie Yahiaoui, Hazem Rashed, Letizia Mariotti, Ganesh Sistu, Ian Clancy, Lucie Yahiaoui, Senthil Yogamani
Fisheyemodnet: Moving Object Detection On Surround-View Cameras For Autonomous Driving, Marie Yahiaoui, Hazem Rashed, Letizia Mariotti, Ganesh Sistu, Ian Clancy, Lucie Yahiaoui, Senthil Yogamani
Session 6: Applications, Architecture and Systems Integration
Moving Object Detection (MOD) is an important task for achieving robust autonomous driving. An autonomous vehicle has to estimate collision risk with other interacting objects in the environment and calculate an optional trajectory. Collision risk is typically higher for moving objects than static ones due to the need to estimate the future states and poses of the objects for decision making. This is particularly important for near-range objects around the vehicle which are typically detected by a fisheye surroundview system that captures a 360± view of the scene. In this work, we propose a CNN architecture for moving object detection …
Fisheyemultinet: Real-Time Multi-Task Learning Architecture For Surround-View Automated Parking System., Pullaro Maddu, Wayne Doherty, Ganesh Sistu, Isabelle Leang, Michal Uricar, Sumanth Chennupati, Hazem Rashed, Jonathan Horgan, Ciaran Hughes, Senthil Yogamani
Fisheyemultinet: Real-Time Multi-Task Learning Architecture For Surround-View Automated Parking System., Pullaro Maddu, Wayne Doherty, Ganesh Sistu, Isabelle Leang, Michal Uricar, Sumanth Chennupati, Hazem Rashed, Jonathan Horgan, Ciaran Hughes, Senthil Yogamani
Session 6: Applications, Architecture and Systems Integration
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 …