activity
20202026
most citedMachine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

91 citations · 152 across the 40 of their papers we have counts for

collaborators

46 papers

cs.CV2026

Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving

Nikos Theodoridis, Reenu Mohandas, Ganesh Sistu +3

The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their reasoning and generalisation capabiliti…

cs.CV2025

Evaluating the Impact of Weather-Induced Sensor Occlusion on BEVFusion for 3D Object Detection

Sanjay Kumar, Tim Brophy, Eoin Martino Grua +3

Accurate 3D object detection is essential for automated vehicles to navigate safely in complex real-world environments. Bird's Eye View (BEV) representations, which project multi-s…

cs.CV2025

SuperQuadricOcc: Real-Time Self-Supervised Semantic Occupancy Estimation with Superquadric Volume Rendering

Seamie Hayes, Alexandre Boulch, Andrei Bursuc +4

Self-supervision for semantic occupancy estimation is appealing as it removes the labour-intensive manual annotation, thus allowing one to scale to larger autonomous driving datase…

cs.CV2025

Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA)

Nikos Theodoridis, Tim Brophy, Reenu Mohandas +4

The remarkable progress of Vision-Language Models (VLMs) on a variety of tasks has raised interest in their application to automated driving. However, for these models to be truste…

cs.CV2025

Evaluating Small Vision-Language Models on Distance-Dependent Traffic Perception

Nikos Theodoridis, Tim Brophy, Reenu Mohandas +4

Vision-Language Models (VLMs) are becoming increasingly powerful, demonstrating strong performance on a variety of tasks that require both visual and textual understanding. Their s…

cs.CV2025

FIN: Fast Inference Network for Map Segmentation

Ruan Bispo, Tim Brophy, Reenu Mohandas +2

Multi-sensor fusion in autonomous vehicles is becoming more common to offer a more robust alternative for several perception tasks. This need arises from the unique contribution of…

cs.CV2025

PAN: Pillars-Attention-Based Network for 3D Object Detection

Ruan Bispo, Dane Mitrev, Letizia Mariotti +4

Camera-radar fusion offers a robust and low-cost alternative to Camera-lidar fusion for the 3D object detection task in real-time under adverse weather and lighting conditions. How…

cs.CV2025

Easy3D-Labels: Supervising Semantic Occupancy Estimation with 3D Pseudo-Labels for Automotive Perception

Seamie Hayes, Ganesh Sistu, Tim Brophy +1

In perception for automated vehicles, safety is critical not only for the driver but also for other agents in the scene, particularly vulnerable road users such as pedestrians and…

cs.CV2025

BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation

Hiep Truong Cong, Ajay Kumar Sigatapu, Arindam Das +4

Accurate motion understanding of the dynamic objects within the scene in bird's-eye-view (BEV) is critical to ensure a reliable obstacle avoidance system and smooth path planning f…

cs.CV2025

Image Segmentation: Inducing graph-based learning

Aryan Singh, Pepijn Van de Ven, Ciarán Eising +1

This study explores the potential of graph neural networks (GNNs) to enhance semantic segmentation across diverse image modalities. We evaluate the effectiveness of a novel GNN-bas…

eess.SP2025

SOLAS: Superpositioning an Optical Lens in Automotive Simulation

Daniel Jakab, Julian Barthel, Alexander Braun +7

Automotive Simulation is a potentially cost-effective strategy to identify and test corner case scenarios in automotive perception. Recent work has shown a significant shift in cre…

cs.CV2025

Revisiting Birds Eye View Perception Models with Frozen Foundation Models: DINOv2 and Metric3Dv2

Seamie Hayes, Ganesh Sistu, Ciarán Eising

Birds Eye View perception models require extensive data to perform and generalize effectively. While traditional datasets often provide abundant driving scenes from diverse locatio…

cs.CV2025★ 1 cited

Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion

Sanjay Kumar, Hiep Truong, Sushil Sharma +4

Autonomous driving technology is rapidly evolving, offering the potential for safer and more efficient transportation. However, the performance of these systems can be significantl…

cs.CV2024

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…

cs.LG2024★ 1 cited

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…

cs.CV2024

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…

cs.RO2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024★ 1 cited

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…

cs.CV2024★ 3 cited

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…

cs.CV2024★ 26 cited

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…

cs.CV2024

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…

cs.CR2024★ 91 cited

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…