32 citations · 108 across the 33 of their papers we have counts for
11 papers · 1 filter
FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System
Pullarao Maddu, Wayne Doherty, Ganesh Sistu +7
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…
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
Michal Uricar, Ganesh Sistu, Hazem Rashed +5
Wide-angle fisheye cameras are commonly used in automated driving for parking and low-speed navigation tasks. Four of such cameras form a surround-view system that provides a compl…
FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving
Hazem Rashed, Mohamed Ramzy, Victor Vaquero +3
Moving object detection is a critical task for autonomous vehicles. As dynamic objects represent higher collision risk than static ones, our own ego-trajectories have to be planned…
FisheyeMODNet: Moving Object detection on Surround-view Cameras for Autonomous Driving
Marie Yahiaoui, Hazem Rashed, Letizia Mariotti +5
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 t…
SoilingNet: Soiling Detection on Automotive Surround-View Cameras
Michal Uricar, Pavel Krizek, Ganesh Sistu +1
Cameras are an essential part of sensor suite in autonomous driving. Surround-view cameras are directly exposed to external environment and are vulnerable to get soiled. Cameras ha…
WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving
Senthil Yogamani, Ciaran Hughes, Jonathan Horgan +14
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of their prevalence…
MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning
Sumanth Chennupati, Ganesh Sistu, Senthil Yogamani +1
Multi-task learning is commonly used in autonomous driving for solving various visual perception tasks. It offers significant benefits in terms of both performance and computationa…
NeurAll: Towards a Unified Visual Perception Model for Automated Driving
Ganesh Sistu, Isabelle Leang, Sumanth Chennupati +4
Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SL…
AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving
Sumanth Chennupati, Ganesh Sistu, Senthil Yogamani +1
Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the…
Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving
Ganesh Sistu, Isabelle Leang, Senthil Yogamani
Convolutional Neural Networks (CNN) are successfully used for various visual perception tasks including bounding box object detection, semantic segmentation, optical flow, depth es…
Multi-stream CNN based Video Semantic Segmentation for Automated Driving
Ganesh Sistu, Sumanth Chennupati, Senthil Yogamani
Majority of semantic segmentation algorithms operate on a single frame even in the case of videos. In this work, the goal is to exploit temporal information within the algorithm mo…