All Outputs (97)

Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction (2021)
Presentation / Conference Contribution
Rainbow, B. A., Men, Q., & Shum, H. P. (2021, October). Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction. Presented at 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia

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.

A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants (2021)
Journal Article
McCay, K. D., Hu, P., Shum, H. P., Woo, W. L., Marcroft, C., Embleton, N. D., Munteanu, A., & Ho, E. S. (2022). A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 8-19. https://doi.org/10.1109/tnsre.2021.3138185

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of aut... Read More about A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants.

PyTorch-based Implementation of Label-aware Graph Representation for Multi-class Trajectory Prediction (2021)
Journal Article
Men, Q., & Shum, H. P. (2022). PyTorch-based Implementation of Label-aware Graph Representation for Multi-class Trajectory Prediction. Software impacts, 11, Article 100201. https://doi.org/10.1016/j.simpa.2021.100201

Trajectory Prediction under diverse patterns has attracted increasing attention in multiple real-world applications ranging from urban traffic analysis to human motion understanding, among which graph convolution network (GCN) is frequently adopted w... Read More about PyTorch-based Implementation of Label-aware Graph Representation for Multi-class Trajectory Prediction.

Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark (2021)
Presentation / Conference Contribution
Isaac-Medina, B. K., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. (2021, October). Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark. Presented at 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada

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)
Presentation / Conference Contribution
Zhou, K., Cheng, Z., Shum, H. P., Li, F. W., & Liang, X. (2021, October). STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising. Presented at 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Bari, Italy

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)
Presentation / Conference Contribution
Crosato, L., Wei, C., Ho, E. S., & Shum, H. P. (2021, September). Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO. Presented at IEEE ICHMS 2021 - 2nd IEEE International Conference on Human-Machine Systems, Magdeburg, Germany

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.

GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interaction (2021)
Journal Article
Men, Q., Shum, H. P., Ho, E. S., & Leung, H. (2022). GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interaction. Computers and Graphics, 102, 634-645. https://doi.org/10.1016/j.cag.2021.09.014

Creating realistic characters that can react to the users’ or another character’s movement can benefit computer graphics, games and virtual reality hugely. However, synthesizing such reactive motions in human-human interactions is a challenging task... Read More about GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interaction.

Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention (2021)
Presentation / Conference Contribution
Zhu, M., Men, Q., Ho, E. S., Leung, H., & Shum, H. P. (2021, July). Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention. Presented at 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Athens, Greece

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.

Spoofing Detection on Hand Images Using Quality Assessment (2021)
Journal Article
Bera, A., Dey, R., Bhattacharjee, D., Nasipuri, M. *., & Shum, H. (2021). Spoofing Detection on Hand Images Using Quality Assessment. Multimedia Tools and Applications, 80(19), 28603-28626. https://doi.org/10.1007/s11042-021-10976-z

Recent research on biometrics focuses on achieving a high success rate of authentication and addressing the concern of various spoofing attacks. Although hand geometry recognition provides adequate security over unauthorized access, it is susceptible... Read More about Spoofing Detection on Hand Images Using Quality Assessment.

Stable Hand Pose Estimation under Tremor via Graph Neural Network (2021)
Presentation / Conference Contribution
Leng, Z., Chen, J., Shum, H. P., Li, F. W., & Liang, X. (2021, March). Stable Hand Pose Estimation under Tremor via Graph Neural Network. Presented at 2021 IEEE Virtual Reality and 3D User Interfaces (VR), Lisboa

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)
Presentation / Conference Contribution
Organisciak, D., Ho, E. S., & Shum, H. P. (2021, January). Makeup Style Transfer on Low-quality Images with Weighted Multi-scale Attention. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

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)
Presentation / Conference Contribution
Men, Q., Hoy, E. S., Shum, H. P., & Leung, H. (2021, January). A Two-Stream Recurrent Network for Skeleton-based Human Interaction Recognition. Presented at 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy

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.

3D car shape reconstruction from a contour sketch using GAN and lazy learning (2021)
Journal Article
Nozawa, N., Shum, H. P., Feng, Q., Ho, E. S., & Morishima, S. (2022). 3D car shape reconstruction from a contour sketch using GAN and lazy learning. Visual Computer, 38(4), 1317-1330. https://doi.org/10.1007/s00371-020-02024-y

3D car models are heavily used in computer games, visual effects, and even automotive designs. As a result, producing such models with minimal labour costs is increasingly more important. To tackle the challenge, we propose a novel system to reconstr... Read More about 3D car shape reconstruction from a contour sketch using GAN and lazy learning.

Two-stage human verification using HandCAPTCHA and anti-spoofed finger biometrics with feature selection (2021)
Journal Article
Bera, A., Bhattacharjee, D., & Shum, H. P. (2021). Two-stage human verification using HandCAPTCHA and anti-spoofed finger biometrics with feature selection. Expert Systems with Applications, 171, https://doi.org/10.1016/j.eswa.2021.114583

This paper presents a human verification scheme in two independent stages to overcome the vulnerabilities of attacks and to enhance security. At the first stage, a hand image-based CAPTCHA (HandCAPTCHA) is tested to avert automated bot-attacks on the... Read More about Two-stage human verification using HandCAPTCHA and anti-spoofed finger biometrics with feature selection.

Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars (2020)
Journal Article
Hu, Y., Shum, H. P., & Ho, E. S. (2020). Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars. IET Intelligent Transport Systems, 14(13), 1845-1854. https://doi.org/10.1049/iet-its.2020.0439

The control of self-driving cars has received growing attention recently. Although existing research shows promising results in the vehicle control using video from a monocular dash camera, there has been very limited work on directly learning vehicl... Read More about Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars.

A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications (2020)
Journal Article
Bhattacharya, M., Roy, S., Mistry, K., Shum, H. P., & Chattopadhyay, S. (2020). A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications. IEEE Access, 8, 221330 - 221351. https://doi.org/10.1109/ACCESS.2020.3043621

The rapid development of mobile internet technology and the better availability of GPS have made mobile online social networks (mOSNs) more popular than traditional online social networks (OSNs) over the last few years. They necessitate fundamental s... Read More about A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications.

A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction (2020)
Journal Article
Men, Q., Ho, E. S., Shum, H. P., & Leung, H. (2021). A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction. IEEE Transactions on Circuits and Systems for Video Technology, 31(9), 3417-3432. https://doi.org/10.1109/tcsvt.2020.3038145

Recurrent neural network (RNN) has become popular for human motion prediction thanks to its ability to capture temporal dependencies. However, it has limited capacity in modeling the complex spatial relationship in the human skeletal structure. In th... Read More about A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction.

Facial reshaping operator for controllable face beautification (2020)
Journal Article
Hu, S., Shum, H. P., Liang, X., Li, F. W., & Aslam, N. (2021). Facial reshaping operator for controllable face beautification. Expert Systems with Applications, 167, Article 114067. https://doi.org/10.1016/j.eswa.2020.114067

Posting attractive facial photos is part of everyday life in the social media era. Motivated by the demand, we propose a lightweight method to automatically and efficiently beautify the shapes of both portrait and non-portrait faces in photos, while... Read More about Facial reshaping operator for controllable face beautification.

LMZMPM: Local Modified Zernike Moment Per-unit Mass for Robust Human Face Recognition (2020)
Journal Article
Kar, A., Pramanik, S., Chakraborty, A., Bhattacharjee, D., Ho, E. S., & Shum, H. P. (2020). LMZMPM: Local Modified Zernike Moment Per-unit Mass for Robust Human Face Recognition. IEEE Transactions on Information Forensics and Security, 16, 495-509. https://doi.org/10.1109/tifs.2020.3015552

In this work, we proposed a novel method, called Local Modified Zernike Moment per unit Mass (LMZMPM), for face recognition, which is invariant to illumination, scaling, noise, in-plane rotation, and translation, along with other orthogonal and inher... Read More about LMZMPM: Local Modified Zernike Moment Per-unit Mass for Robust Human Face Recognition.

Cumuliform Cloud Formation Control using Parameter-Predicting Convolutional Neural Network (2020)
Journal Article
Zhang, Z., Ma, Y., Li, Y., Li, F. W., Shum, H. P., Yang, B., Guo, J., & Liang, X. (2020). Cumuliform Cloud Formation Control using Parameter-Predicting Convolutional Neural Network. Graphical Models, 111, Article 101083. https://doi.org/10.1016/j.gmod.2020.101083

Physically-based cloud simulation is an effective approach for synthesizing realistic cloud. However, generating clouds with desired shapes requires a time-consuming process for selecting the appropriate simulation parameters. This paper addresses su... Read More about Cumuliform Cloud Formation Control using Parameter-Predicting Convolutional Neural Network.