16 citations · 38 across the 10 of their papers we have counts for
13 papers
Band-Selection Stability and Semantic Segmentation Performance: A Study on Hyperspectral City
Jiarong Li, Imad Ali Shah, Enda Ward +3
Resource constraints make high-dimensional hyperspectral imaging challenging in autonomous perception, motivating the use of band selection methods. However, the sensitivity of ban…
HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation
Imad Ali Shah, Imran Mehmood, Enda Ward +3
The HSI-Road dataset provides paired RGB and 25-channel NIR (680-960nm) images with binary masks but no surface-level labels. This paper introduces a manually labeled six-class tax…
Learnable Quantum Efficiency Filters for Urban Hyperspectral Segmentation
Imad Ali Shah, Jiarong Li, Ethan Delaney +4
Hyperspectral sensing provides rich spectral information for scene understanding in urban driving, but its high dimensionality poses challenges for interpretation and efficient lea…
Hyperspectral Sensors and Autonomous Driving: Technologies, Limitations, and Opportunities
Imad Ali Shah, Jiarong Li, Roshan George +5
Hyperspectral imaging (HSI) offers a transformative sensing modality for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD) applications, enabling material-level…
Hyperspectral vs. RGB for Pedestrian Segmentation in Urban Driving Scenes: A Comparative Study
Jiarong Li, Imad Ali Shah, Enda Ward +3
Pedestrian segmentation in automotive perception systems faces critical safety challenges due to metamerism in RGB imaging, where pedestrians and backgrounds appear visually indist…
CSNR and JMIM Based Spectral Band Selection for Reducing Metamerism in Urban Driving
Jiarong Li, Imad Ali Shah, Diarmaid Geever +5
Protecting Vulnerable Road Users (VRU) is a critical safety challenge for automotive perception systems, particularly under visual ambiguity caused by metamerism, a phenomenon wher…
Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarios
Imad Ali Shah, Jiarong Li, Tim Brophy +4
Recent advances in autonomous driving (AD) have highlighted the potential of hyperspectral imaging (HSI) for enhanced environmental perception, particularly in challenging weather…
Hyperspectral Imaging-Based Perception in Autonomous Driving Scenarios: Benchmarking Baseline Semantic Segmentation Models
Imad Ali Shah, Jiarong Li, Martin Glavin +3
Hyperspectral Imaging (HSI) is known for its advantages over traditional RGB imaging in remote sensing, agriculture, and medicine. Recently, it has gained attention for enhancing A…
Revisiting Modality Imbalance In Multimodal Pedestrian Detection
Arindam Das, Sudip Das, Ganesh Sistu +5
Multimodal learning, particularly for pedestrian detection, has recently received emphasis due to its capability to function equally well in several critical autonomous driving sce…
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation
Arindam Das, Sudip Das, Ganesh Sistu +5
Most of the existing works on pedestrian pose estimation do not consider estimating the pose of an occluded pedestrian, as the annotations of the occluded parts are not available i…
An Objective Method for Pedestrian Occlusion Level Classification
Shane Gilroy, Martin Glavin, Edward Jones +1
Pedestrian detection is among the most safety-critical features of driver assistance systems for autonomous vehicles. One of the most complex detection challenges is that of partia…
E-Scooter Rider Detection and Classification in Dense Urban Environments
Shane Gilroy, Darragh Mullins, Edward Jones +2
Accurate detection and classification of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic. Although similar…
The Impact of Partial Occlusion on Pedestrian Detectability
Shane Gilroy, Darragh Mullins, Edward Jones +2
Robust detection of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic. One of the most complex outstanding c…