91 citations · 152 across the 40 of their papers we have counts for
13 papers · 1 filter
Reflective Teacher: Semi-Supervised Multimodal 3D Object Detection in Bird's-Eye-View via Uncertainty Measure
Saheli Hazra, Sudip Das, Rohit Choudhary +4
Applying pseudo labeling techniques has been found to be advantageous in semi-supervised 3D object detection (SSOD) in Bird's-Eye-View (BEV) for autonomous driving, particularly wh…
Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT
Mirza Akhi Khatun, Mangolika Bhattacharya, Ciarán Eising +1
This research develops a new method to detect anomalies in time series data using Convolutional Neural Networks (CNNs) in healthcare-IoT. The proposed method creates a Distributed…
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…
Velocity Driven Vision: Asynchronous Sensor Fusion Birds Eye View Models for Autonomous Vehicles
Seamie Hayes, Sushil Sharma, Ciarán Eising
Fusing different sensor modalities can be a difficult task, particularly if they are asynchronous. Asynchronisation may arise due to long processing times or improper synchronisati…
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…
Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird's-Eye-View
Arindam Das, Sudarshan Paul, Niko Scholz +4
Accurate obstacle identification represents a fundamental challenge within the scope of near-field perception for autonomous driving. Conventionally, fisheye cameras are frequently…
Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation
Mirza Akhi Khatun, Sanober Farheen Memon, Ciarán Eising +1
The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a data-driven infrastructure that highly relies on smart sensing devices (i.e., blood pressure m…
Measuring Natural Scenes SFR of Automotive Fisheye Cameras
Daniel Jakab, Eoin Martino Grua, Brian Micheal Deegan +3
The Modulation Transfer Function (MTF) is an important image quality metric typically used in the automotive domain. However, despite the fact that optical quality has an impact on…
Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach
Sushil Sharma, Aryan Singh, Ganesh Sistu +2
Predicting the trajectory of an ego vehicle is a critical component of autonomous driving systems. Current state-of-the-art methods typically rely on Deep Neural Networks (DNNs) an…