114 citations · 579 across the 79 of their papers we have counts for
13 papers · 2 filters
Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments
Bruna G. Maciel-Pearson, Letizia Marchegiani, Samet Akcay +3
With the rapidly growing expansion in the use of UAVs, the ability to autonomously navigate in varying environments and weather conditions remains a highly desirable but as-of-yet…
Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection
Ganesh Samarth C. A., Neelanjan Bhowmik, Toby P. Breckon
In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imag…
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery
Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akcay +1
X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items. Particular interest lies in the automatic d…
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery
Neelanjan Bhowmik, Yona Falinie A. Gaus, Samet Akcay +2
X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on t…
Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
Qian Wang, Toby P. Breckon
Automatic crowd behaviour analysis is an important task for intelligent transportation systems to enable effective flow control and dynamic route planning for varying road particip…
The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks for Prohibited Item Detection Using Real and Synthetically Composited X-ray Imagery
Neelanjan Bhowmik, Qian Wang, Yona Falinie A. Gaus +2
Detecting prohibited items in X-ray security imagery is pivotal in maintaining border and transport security against a wide range of threat profiles. Convolutional Neural Networks…
To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation
Amir Atapour-Abarghouei, Toby P. Breckon
Robust three-dimensional scene understanding is now an ever-growing area of research highly relevant in many real-world applications such as autonomous driving and robotic navigati…
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery
Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akçay +3
X-ray baggage security screening is widely used to maintain aviation and transport security. Of particular interest is the focus on automated security X-ray analysis for particular…
Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach
Amir Atapour-Abarghouei, Toby P. Breckon
Robust geometric and semantic scene understanding is ever more important in many real-world applications such as autonomous driving and robotic navigation. In this paper, we propos…
An Approach for Adaptive Automatic Threat Recognition Within 3D Computed Tomography Images for Baggage Security Screening
Qian Wang, Khalid N. Ismail, Toby P. Breckon
The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, kno…
Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition
Qian Wang, Penghui Bu, Toby P. Breckon
Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling in…
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
Samet Akçay, Amir Atapour-Abarghouei, Toby P. Breckon
Despite inherent ill-definition, anomaly detection is a research endeavor of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly det…
DeGraF-Flow: Extending DeGraF Features for accurate and efficient sparse-to-dense optical flow estimation
Felix Stephenson, Toby Breckon, Ioannis Katramados
Modern optical flow methods make use of salient scene feature points detected and matched within the scene as a basis for sparse-to-dense optical flow estimation. Current feature d…