64 citations · 224 across the 21 of their papers we have counts for
23 papers
Navigating human-robot interaction challenges in private and residential aged care homes
David Ahmedt-Aristizabal, Mohammad Ali Armin, Akila Pemasiri +4
This chapter explores how robotic systems and ambient intelligence can enhance care for older adults in residential aged care (RAC) homes and private homes. Although these settings…
Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers
Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando +6
Adversarial vulnerabilities remain a major concern for the safe deployment of neural networks, particularly in object detection, a core task embedded in many safety-critical system…
Physics-Guided Attention in a Lightweight TCN for Efficient WiFi CSI-Based Human Activity Recognition
Chinthaka Ranasingha, Tharindu Fernando, Sridha Sridharan +2
Human Action Recognition (HAR) using WiFi Channel State Information (CSI) has gained increasing attention due to its non-contact, low-cost, and privacy-preserving nature. However,…
Bi Directional Feedback Fusion for Activity Aware Forecasting of Indoor CO2 and PM2.5
Harshala Gammulle, Lidia Morawska, Sridha Sridharan +1
Indoor air quality (IAQ) forecasting plays a critical role in safeguarding occupant health, ensuring thermal comfort, and supporting intelligent building control. However, predicti…
Transforming volcanic monitoring: A dataset and benchmark for onboard volcano activity detection
Darshana Priyasad, Tharindu Fernando, Maryam Haghighat +2
Natural disasters, such as volcanic eruptions, pose significant challenges to daily life and incur considerable global economic losses. The emergence of next-generation small-satel…
Dual-Stream Alignment for Action Segmentation
Harshala Gammulle, Clinton Fookes, Sridha Sridharan +1
Action segmentation is a challenging yet active research area that involves identifying when and where specific actions occur in continuous video streams. Most existing work has fo…
Physics Augmented Tuple Transformer for Autism Severity Level Detection
Chinthaka Ranasingha, Harshala Gammulle, Tharindu Fernando +2
Early diagnosis of Autism Spectrum Disorder (ASD) is an effective and favorable step towards enhancing the health and well-being of children with ASD. Manual ASD diagnosis testing…
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification
Harshala Gammulle, Yubo Chen, Sridha Sridharan +2
Endoscopy plays a major role in identifying any underlying abnormalities within the gastrointestinal (GI) tract. There are multiple GI tract diseases that are life-threatening, suc…
Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction
Tharindu Fernando, Harshala Gammulle, Sridha Sridharan +2
Humans exhibit complex motions that vary depending on the task that they are performing, the interactions they engage in, as well as subject-specific preferences. Therefore, foreca…
Vision-Based Activity Recognition in Children with Autism-Related Behaviors
Pengbo Wei, David Ahmedt-Aristizabal, Harshala Gammulle +2
Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting. In particular, several deep learning systems ha…
Towards On-Board Panoptic Segmentation of Multispectral Satellite Images
Tharindu Fernando, Clinton Fookes, Harshala Gammulle +2
With tremendous advancements in low-power embedded computing devices and remote sensing instruments, the traditional satellite image processing pipeline which includes an expensive…
Continuous Human Action Recognition for Human-Machine Interaction: A Review
Harshala Gammulle, David Ahmedt-Aristizabal, Simon Denman +3
With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams…
Multi-Slice Net: A novel light weight framework for COVID-19 Diagnosis
Harshala Gammulle, Tharindu Fernando, Sridha Sridharan +2
This paper presents a novel lightweight COVID-19 diagnosis framework using CT scans. Our system utilises a novel two-stage approach to generate robust and efficient diagnoses acros…
Deep Learning for Medical Anomaly Detection -- A Survey
Tharindu Fernando, Harshala Gammulle, Simon Denman +2
Machine learning-based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical applicatio…
Multi-modal Fusion for Single-Stage Continuous Gesture Recognition
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
Gesture recognition is a much studied research area which has myriad real-world applications including robotics and human-machine interaction. Current gesture recognition methods h…
Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
Automating the analysis of imagery of the Gastrointestinal (GI) tract captured during endoscopy procedures has substantial potential benefits for patients, as it can provide diagno…
Hierarchical Attention Network for Action Segmentation
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video. Several attempts have been made to capture fra…
Predicting the Future: A Jointly Learnt Model for Action Anticipation
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
Inspired by human neurological structures for action anticipation, we present an action anticipation model that enables the prediction of plausible future actions by forecasting bo…
Coupled Generative Adversarial Network for Continuous Fine-grained Action Segmentation
Harshala Gammulle, Tharindu Fernando, Simon Denman +2
We propose a novel conditional GAN (cGAN) model for continuous fine-grained human action segmentation, that utilises multi-modal data and learned scene context information. The pro…
Forecasting Future Action Sequences with Neural Memory Networks
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
We propose a novel neural memory network based framework for future action sequence forecasting. This is a challenging task where we have to consider short-term, within sequence re…
Fine-grained Action Segmentation using the Semi-Supervised Action GAN
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
In this paper we address the problem of continuous fine-grained action segmentation, in which multiple actions are present in an unsegmented video stream. The challenge for this ta…
Multi-Level Sequence GAN for Group Activity Recognition
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
We propose a novel semi-supervised, Multi-Level Sequential Generative Adversarial Network (MLS-GAN) architecture for group activity recognition. In contrast to previous works which…
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition
Harshala Gammulle, Simon Denman, Sridha Sridharan +1
In this paper we address the problem of human action recognition from video sequences. Inspired by the exemplary results obtained via automatic feature learning and deep learning a…