Ciarán Eising

University of Limerick | UL · Department of Electronic and Computer Engineering

Measuring optical quality in camera lenses is crucial in evaluating cameras, especially for safety-critical visual perception tasks in automotive driving. While ground-truth labels and annotations are provided in publicly available automotive datasets for computer vision tasks, there is a lack of information on the image quality of camera lenses us...

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 and ADAS. The automotive industry has advanced in applying state-of-the-art computer vision to enhance road safety and provide automated driving functionality. When using c...

I had the pleasure of presenting my poster titled "Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT" at the IEEE 12th International Conference on Healthcare Informatics (ICHI 2024) at the University of Florida, Orlando, USA.

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 Denial of Service (DDoS) attack using an IoT network simulator, Cooja, which emulates environmental sensors such as temperature and humidity. CNNs detect anomalies in tim...

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 Denial of Service (DDoS) attack using an IoT network simulator, Cooja, which emulates environmental sensors such as temperature and humidity. CNNs detect anomalies in tim...

Fusing different sensor modalities can be a difficult task, particularly if they are asynchronous. Asynchronisation may arise due to long processing times or improper synchronisation during calibration, and there must exist a way to still utilise this previous information for the purpose of safe driving, and object detection in ego vehicle/ multi-a...

This study investigates the effectiveness of modern Deformable Convolutional Neural Networks (DCNNs) for semantic segmentation tasks, particularly in autonomous driving scenarios with fisheye images. These images, providing a wide field of view, pose unique challenges for extracting spatial and geometric information due to dynamic changes in object...

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 automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations...

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 extraction and Graph Neural Networks (GNNs) for structural modeling. GSN employs k-means clustering to group graph nodes into clusters, facilitating the creation of subgraph...

Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimodal trajectory prediction enhances decision-making by considering multiple possible future trajectories based on diverse sources of environmental data. In this approach,...

In recent years, significant advances have been made in the development of Advanced Driver Assistance Systems (ADAS) and other technology for autonomous vehicles. Automated object detection is a crucial component of autonomous driving; however, there are still known issues that affect its performance. For automotive applications, object detection a...

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 alongside textual queries, fostering natural interaction and trust between the vehicle and its occupants or other road users. This study investigates the attention patterns...

Purpose : In previous work, we applied DenseNet architecture for classifying papilledema severity. This model correctly estimated swelling severity within one Frisén grade in 71.8% of cases. The present study explores the potential of Vision Transformers (ViT) as a novel methodology to discern papilledema from normal ocular fundus images and to cla...

Image decolorization is an image pre-processing step which is widely used in image analysis, computer vision, and printing applications. The most commonly used methods give each color channel (e.g., the R component in RGB format, or the Y component of an image in CIE-XYZ format) a constant weight without considering image content. This approach is...

Autonomous vehicles and Intelligent Transport Systems (ITS) have started to become a reality in recent years. However, shortcomings of these early intelligent vehicles demonstrate a need to increase an intelligent vehicle’s perceptual bubble beyond the vehicle’s onboard sensors. Vehicle-to-Everything (V2X) communications is a technology intended to...

Supervised deep learning methods have produced state-of-the-art results with large labeled datasets. However, accessing large labeled datasets is difficult in medical image analysis because of a shortage of medical experts, expensive annotations, and privacy constraints in the healthcare domain. Self-supervised learning is a branch of machine learn...

The development of vision and language transformer models has paved the way for Visual Question Answering (VQA) models and related research. There are metrics to assess the general accuracy of VQA models but subjective assessment of the answers generated by the models is necessary to gain an in-depth understanding and a framework for subjective ass...

Graph Neural Networks (GNNs) offer a promising direction for medical image analysis, particularly due to their ability to capture complex relationships and handle non-Euclidean data structures often encountered in this domain. This study aims to demonstrate the potential of GNNs in medical image classification, showcasing their comparable performan...

As the global production of waste continues to rise, there is a growing demand for more effective waste management strategies to handle this expanding problem. Recycling rates in the United States for recyclable materials are below 35%, resulting in elevated levels of waste being sent to landfills. This situation has alarming consequences, contribu...

Predicting the trajectory of the ego vehicle is a critical task for autonomous vehicles. Even though traffic regulations have defined boundaries, various behaviors of the agents in real-life situations introduce complexities that are hard to capture comprehensively. This has led to a rising curiosity in ego vehicle trajectory prediction based on le...

Deep learning-based computer vision models are typically data-hungry, resulting in the rise of dataset sizes. The consensus for computer vision datasets is that larger datasets lead to better model performance. However, the quality of the datasets is often not considered. Annotating datasets for fully supervised object detection and instance segmen...

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 monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more v...

In advanced driver assistance systems (ADAS) or autonomous vehicle research, acquiring semantic information about the surrounding environment generally relies heavily on camera-based object detection. Image signal processors (ISPs) in cameras are generally tuned for human perception. In most cases, ISP parameters are selected subjectively and the r...

Deep learning models have demonstrated remarkable results for various computer vision tasks, including the realm of medical imaging. However, their application in the medical domain is limited due to the requirement for large amounts of training data, which can be both challenging and expensive to obtain. To mitigate this, pre-trained models have b...

Camera-based perception systems play a central role in modern autonomous vehicles. These camera based perception algorithms require an accurate calibration to map the real world distances to image pixels. In practice, calibration is a laborious procedure requiring specialised data collection and careful tuning. This process must be repeated wheneve...

Computing platforms in autonomous vehicles record large amounts of data from many sensors, process the data through machine learning models, and make decisions to ensure the vehicle's safe operation. Fast, accurate, and reliable decision-making is critical. Traditional computer processors lack the power and flexibility needed for the perception and...

Graph-based neural network models are gaining traction in the field of representation learning due to their ability to uncover latent topological relationships between entities that are otherwise challenging to identify. These models have been employed across a diverse range of domains, encompassing drug discovery, protein interactions, semantic se...

This short paper presents a preliminary analysis of three popular Visual Question Answering (VQA) models, namely ViLBERT, ViLT, and LXMERT, in the context of answering questions relating to driving scenarios. The performance of these models is evaluated by comparing the similarity of responses to reference answers provided by computer vision expert...

Advancements in intelligent vehicles and Intelligent Transport Systems (ITS) have shown that they are now feasible in both technology and commerce. However, there are still significant challenges to overcome, particularly regarding the perception and coordination of intelligent vehicles in unfavourable conditions. Vehicle-to-Everything (V2X) commun...

Autonomous vehicles require accurate and reliable short-term trajectory predictions for safe and efficient driving. While most commercial automated vehicles currently use state machine-based algorithms for trajectory forecasting, recent efforts have focused on end-to-end data-driven systems. Often, the design of these models is limited by the avail...

Automated vehicles rely heavily on image data from visible spectrum cameras to perform a wide range of tasks from object detection, classification, and avoidance to path planning. The availability and reliability of these sensors in adverse weather is therefore of critical importance to the safe and continuous operation of an automated vehicle. Thi...

As the amount of waste being produced globally is increasing, there is a need for more efficient waste management solutions to accommodate this expansion. The first step in waste management is the collection of bins or containers. Each bin truck in a fleet is assigned a collection route. As the bin trucks have a finite amount of storage for waste,...

Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover $360^\circ$ around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited dataset...

Interacting with other roads users is a challenge for an autonomous vehicle, particularly in urban areas. Existing vehicle systems behave in a reactive manner, warning the driver or applying the brakes when the pedestrian is already in front of the vehicle. The ability to anticipate a pedestrian’s crossing intention ahead of time will result in saf...

Multimodal learning, particularly for pedestrian detection, has recently received emphasis due to its capability to function equally well in several critical autonomous driving scenarios such as low-light, night-time, and adverse weather conditions. However, in most cases, the training distribution largely emphasizes the contribution of one specifi...

Optimizing exposure time for low light scenarios involves a trade-off between motion blur and signal to noise ratio. A method for defining the optimum exposure time for a given function has not been described in the literature. This paper presents the design of a simulation of motion blur and exposure time from the perspective of a real-world camer...

Fully-supervised object detection and instance segmentation models have accomplished notable results on large-scale computer vision benchmark datasets. However, fully-supervised machine learning algorithms’ performances are immensely dependent on the quality of the training data. Preparing computer vision datasets for object detection and instance...

In the field of autonomous driving, cameras are crucial sensors for providing information about a vehicle’s environment. Image quality refers to a camera system’s ability to capture, process, and display signals to form an image. Historically, "good quality" in this context refers to images that have been processed by an Image Signal Processor (ISP...

Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e.g., autonomous driving. A solution to this would be to eliminate shadow regions from the images before the processing of the perception system. Yet, training such a solution requires pairs of...

Electric Vehicles are increasingly common, with inductive chargepads being considered a convenient and efficient means of charging electric vehicles. However, drivers are typically poor at aligning the vehicle to the necessary accuracy for efficient inductive charging, making the automated alignment of the two charging plates desirable. In parallel...

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 in relevant automotive datasets. For example, CityPersons, a well-known dataset for pedestrian detection in automotive scenes does not provide pose annotations, whereas MS...

With the ever-increasing electrification of the vehicle showing no sign of retreating, electronic systems deployed in automotive applications are subject to more stringent Electromagnetic Immunity compliance constraints than ever before, to ensure the proximity of nearby electronic systems will not affect their operation. The EMI compliance testing...

With the ever-increasing electrification of the vehicle showing no sign of retreating, electronic systems deployed in automotive applications are subject to more stringent Electromagnetic Immunity compliance constraints than ever before, to ensure the proximity of nearby electronic systems will not affect their operation. The EMI compliance testing...

Most of the existing works on pedestrian pose estimation do not consider estimating the pose of an occluded pedestrians, as the annotations of the occluded parts are not available in relevant automotive datasets. For example, CityPersons, a well-known dataset for pedestrian detection in automotive scenes does not provide pose annotations, whereas M...

In this paper, it is proposed to solve the problem of triangulation for calibrated omnidirectional cameras through the optimisation of ray-pairs on the projective sphere. The proposed solution boils down to finding the roots of a quadratic function, and as such is closed form, completely non-iterative and computationally inexpensive when compared t...

Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360{\deg} around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets an...

Self-supervised learning has been an active area of research in the past few years. Contrastive learning is a type of self-supervised learning method that has achieved a significant performance improvement on image classification task. However, there has been no work done in its application to fisheye images for autonomous driving. In this paper, w...

Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround-view camera systems typically comprise of four fisheye cameras with 190°+ field of view covering the entire 360° around the vehicle focused on near-field sensin...

Abstract It is well understood that in ADAS applications, a good estimate of the pose of the vehicle is required. This paper proposes a metaphorically named 2.5D odometry, whereby the planar odometry derived from the yaw rate sensor and four wheel speed sensors is augmented by a linear model of suspension. While the core of the planar odometry is a...

It is well understood that in ADAS applications, a good estimate of the pose of the vehicle is required. This paper proposes a metaphorically named 2.5D odometry, whereby the planar odometry derived from the yaw rate sensor and four wheel speed sensors is augmented by a linear model of suspension. While the core of the planar odometry is a yaw rate...

Electric Vehicles are increasingly common, with inductive chargepads being considered a convenient and efficient means of charging electric vehicles. However, drivers are typically poor at aligning the vehicle to the necessary accuracy for efficient inductive charging, making the automated alignment of the two charging plates desirable. In parallel...

Automated driving is an active area of research in both industry and academia. Automated Parking, which is automated driving in a restricted scenario of parking with low speed manoeuvring, is a key enabling product for fully autonomous driving systems. It is also an important milestone from the perspective of a higher end system built from the prev...

We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automoated driving. Three commonly used geometric constraints in pin-hole imagery (the positive height, positive depth and epipolar constraints) are reformulated to spherical coordinates, making them invariant to specific camera configurations as l...

Vision-based driver assistance systems is one of the rapidly growing research areas of ITS, due to various factors such as the increased level of safety requirements in automotive, computational power in embedded systems, and desire to get closer to autonomous driving. It is a cross disciplinary area encompassing specialised fields like computer vi...