Outputs (20)

DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications (2021)
Conference Proceeding
Li, L., Ismail, K. N., Shum, H. P., & Breckon, T. P. (2021). DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications. . https://doi.org/10.1109/3dv53792.2021.00130

We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is eq... Read More about DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications.

Continuous Multi-modal Emotion Prediction in Video based on Recurrent Neural Network Variants with Attention (2021)
Conference Proceeding
Raju, J., Gaus, Y., & Breckon, T. (2021). Continuous Multi-modal Emotion Prediction in Video based on Recurrent Neural Network Variants with Attention. . https://doi.org/10.1109/icmla52953.2021.00115

Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery (2021)
Conference Proceeding
Wang, Q., & Breckon, T. (2021). Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery. . https://doi.org/10.1109/icmla52953.2021.00020

Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery (2021)
Conference Proceeding
Webb, T., Bhowmik, N., Gaus, Y., & Breckon, T. (2021). Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery. . https://doi.org/10.1109/icmla52953.2021.00102

Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition (2021)
Conference Proceeding
Alsehaim, A., & Breckon, T. (2021). Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition.

We uniquely consider the task of joint person re-identification (Re-ID) and action recognition in video as a multi-task problem. In addition to the broader potential of joint Re-ID and action recognition within the context of automated multi-camera s... Read More about Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition.

Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark (2021)
Conference Proceeding
Isaac-Medina, B. K., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. (2021). Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark. . https://doi.org/10.1109/iccvw54120.2021.00142

Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due to both negligent and malicious use. For this reason, the automated detection and tracking of UAV is a fundamental task in aerial security systems. Common technologies for... Read More about Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark.

PANDA: Perceptually Aware Neural Detection of Anomalies (2021)
Conference Proceeding
Barker, J., & Breckon, T. (2021). PANDA: Perceptually Aware Neural Detection of Anomalies. . https://doi.org/10.1109/ijcnn52387.2021.9534399

Semi-supervised methods of anomaly detection have seen substantial advancement in recent years. Of particular interest are applications of such methods to diverse, real-world anomaly detection problems where anomalous variations can vary from the vis... Read More about PANDA: Perceptually Aware Neural Detection of Anomalies.

On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening (2021)
Conference Proceeding
Wang, Q., & Breckon, T. (2021). On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening. In 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings. https://doi.org/10.1109/ijcnn52387.2021.9533631

We address the automatic contraband material detection problem within volumetric 3D Computed Tomography (CT) data for baggage security screening. Distinct from the prohibited item detection using object detection techniques, contraband material detec... Read More about On the Evaluation of Semi-Supervised 2D Segmentation for Volumetric 3D Computed Tomography Baggage Security Screening.

Autoencoders Without Reconstruction for Textural Anomaly Detection (2021)
Conference Proceeding
Adey, P., Akcay, S., Bordewich, M., & Breckon, T. (2021). Autoencoders Without Reconstruction for Textural Anomaly Detection. . https://doi.org/10.1109/ijcnn52387.2021.9533804

Automatic anomaly detection in natural textures is a key component within quality control for a range of high-speed, high-yield manufacturing industries that rely on camera-based visual inspection techniques. Targeting anomaly detection through the u... Read More about Autoencoders Without Reconstruction for Textural Anomaly Detection.

On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks (2021)
Conference Proceeding
Bhowmik, N., Gaus, Y., & Breckon, T. (2021). On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks.

Automatic detection of prohibited items within complex and cluttered X-ray security imagery is essential to maintaining transport security, where prior work on automatic prohibited item detection focus primarily on pseudo-colour (rgb) X-ray imagery.... Read More about On the Impact of Using X-Ray Energy Response Imagery for Object Detection via Convolutional Neural Networks.