Outputs (10)

Bi-projection-based Foreground-aware Omnidirectional Depth Prediction (2021)
Conference Proceeding
Feng, Q., Shum, H. P., & Morishima, S. (2021). Bi-projection-based Foreground-aware Omnidirectional Depth Prediction.

Due to the increasing availability of commercial 360- degree cameras, accurate depth prediction for omnidirectional images can be beneficial to a wide range of applications including video editing and augmented reality. Regarding existing methods, so... Read More about Bi-projection-based Foreground-aware Omnidirectional Depth Prediction.

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.

Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction (2021)
Conference Proceeding
Rainbow, B. A., Men, Q., & Shum, H. P. (2021). Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction. . https://doi.org/10.1109/smc52423.2021.9658781

Predicting the movement trajectories of multiple classes of road users in real-world scenarios is a challenging task due to the diverse trajectory patterns. While recent works of pedestrian trajectory prediction successfully modelled the influence of... Read More about Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction.

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.

STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising (2021)
Conference Proceeding
Zhou, K., Cheng, Z., Shum, H. P., Li, F. W., & Liang, X. (2021). STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising. . https://doi.org/10.1109/ismar52148.2021.00018

Hand object interaction in mixed reality (MR) relies on the accurate tracking and estimation of human hands, which provide users with a sense of immersion. However, raw captured hand motion data always contains errors such as joints occlusion, disloc... Read More about STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising.

Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO (2021)
Conference Proceeding
Crosato, L., Wei, C., Ho, E. S., & Shum, H. P. (2021). Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO. . https://doi.org/10.1109/ichms53169.2021.9582640

As Autonomous Vehicles (AV) are becoming a reality, the design of efficient motion control algorithms will have to deal with the unpredictable and interactive nature of other road users. Current AV motion planning algorithms suffer from the freezing... Read More about Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO.

Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention (2021)
Conference Proceeding
Zhu, M., Men, Q., Ho, E. S., Leung, H., & Shum, H. P. (2021). Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention. . https://doi.org/10.1109/bhi50953.2021.9508619

Early prediction of cerebral palsy is essential as it leads to early treatment and monitoring. Deep learning has shown promising results in biomedical engineering thanks to its capacity of modelling complicated data with its non-linear architecture.... Read More about Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention.

Stable Hand Pose Estimation under Tremor via Graph Neural Network (2021)
Conference Proceeding
Leng, Z., Chen, J., Shum, H. P., Li, F. W., & Liang, X. (2021). Stable Hand Pose Estimation under Tremor via Graph Neural Network. In 2021 IEEE Virtual Reality and 3D User Interfaces (VR) (226-234). https://doi.org/10.1109/vr50410.2021.00044

Hand pose estimation, which predicts the spatial location of hand joints, is a fundamental task in VR/AR applications. Although existing methods can recover hand pose competently, the tremor issue occurring in hand motion has not been completely solv... Read More about Stable Hand Pose Estimation under Tremor via Graph Neural Network.

Makeup Style Transfer on Low-quality Images with Weighted Multi-scale Attention (2021)
Conference Proceeding
Organisciak, D., Ho, E. S., & Shum, H. P. (2021). Makeup Style Transfer on Low-quality Images with Weighted Multi-scale Attention. . https://doi.org/10.1109/icpr48806.2021.9412604

Facial makeup style transfer is an extremely challenging sub-field of image-to-image-translation. Due to this difficulty, state-of-the-art results are mostly reliant on the Face Parsing Algorithm, which segments a face into parts in order to easily e... Read More about Makeup Style Transfer on Low-quality Images with Weighted Multi-scale Attention.

A Two-Stream Recurrent Network for Skeleton-based Human Interaction Recognition (2021)
Conference Proceeding
Men, Q., Hoy, E. S., Shum, H. P., & Leung, H. (2021). A Two-Stream Recurrent Network for Skeleton-based Human Interaction Recognition. . https://doi.org/10.1109/icpr48806.2021.9412538

This paper addresses the problem of recognizing human-human interaction from skeletal sequences. Existing methods are mainly designed to classify single human action. Many of them simply stack the movement features of two characters to deal with huma... Read More about A Two-Stream Recurrent Network for Skeleton-based Human Interaction Recognition.