91 citations · 125 across the 17 of their papers we have counts for
14 papers · 1 filter
Deformable Convolution Based Road Scene Semantic Segmentation of Fisheye Images in Autonomous Driving
Anam Manzoor, Aryan Singh, Ganesh Sistu +4
This study investigates the effectiveness of modern Deformable Convolutional Neural Networks (DCNNs) for semantic segmentation tasks, particularly in autonomous driving scenarios w…
SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection
Daniel Jakab, Alexander Braun, Cathaoir Agnew +6
Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of autom…
Subgraph Clustering and Atom Learning for Improved Image Classification
Aryan Singh, Pepijn Van de Ven, Ciarán Eising +1
In this study, we present the Graph Sub-Graph Network (GSN), a novel hybrid image classification model merging the strengths of Convolutional Neural Networks (CNNs) for feature ext…
MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles
Sushil Sharma, Arindam Das, Ganesh Sistu +2
Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimo…
Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns
Kaavya Rekanar, Martin Hayes, Ganesh Sistu +1
Visual Question Answering (VQA) models play a critical role in enhancing the perception capabilities of autonomous driving systems by allowing vehicles to analyze visual inputs alo…
Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges
Daniel Jakab, Brian Michael Deegan, Sushil Sharma +6
In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving an…