Publications
8 results found
Zhang X, Demiris Y, 2023, Visible and Infrared Image Fusion using Deep Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence
Zhang X, Angeloudis P, Demiris Y, 2023, Dual-branch Spatio-Temporal Graph Neural Networks for Pedestrian Trajectory Prediction, Pattern Recognition, ISSN: 0031-3203
Zhang X, Angeloudis P, Demiris Y, 2022, ST CrossingPose: a spatial-temporal graph convolutional network for skeleton-based pedestrian crossing intention prediction, IEEE Transactions on Intelligent Transportation Systems, Vol: 23, Pages: 20773-20782, ISSN: 1524-9050
Pedestrian crossing intention prediction is crucial for the safety of pedestrians in the context of both autonomous and conventional vehicles and has attracted widespread interest recently. Various methods have been proposed to perform pedestrian crossing intention prediction, among which the skeleton-based methods have been very popular in recent years. However, most existing studies utilize manually designed features to handle skeleton data, limiting the performance of these methods. To solve this issue, we propose to predict pedestrian crossing intention based on spatial-temporal graph convolutional networks using skeleton data (ST CrossingPose). The proposed method can learn both spatial and temporal patterns from skeleton data, thus having a good feature representation ability. Extensive experiments on a public dataset demonstrate that the proposed method achieves very competitive performance in predicting crossing intention while maintaining a fast inference speed. We also analyze the effect of several factors, e.g., size of pedestrians, time to event, and occlusion, on the proposed method.
Zhang X, Feng Y, Angeloudis P, et al., 2022, Monocular visual traffic surveillance: a review, IEEE Transactions on Intelligent Transportation Systems, Vol: 23, Pages: 14148-14165, ISSN: 1524-9050
To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined.
Zhang X, 2022, Deep Learning-based Multi-focus Image Fusion: A Survey and A Comparative Study, IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN: 0162-8828
Zhang X, 2021, Benchmarking and Comparing Multi-exposure Image Fusion Algorithms, Information Fusion, Vol: 74, Pages: 111-131, ISSN: 1566-2535
Zhang X, Ye P, Leung H, et al., 2020, Object fusion tracking based on visible and infrared images: A comprehensive review, Information Fusion, Vol: 63, Pages: 166-187, ISSN: 1566-2535
Stathaki T, 2008, Image Fusion, Publisher: Academic Press, ISBN: 9780123725295
This book will be an invaluable resource to R&D engineers, academic researchers and system developers requiring the most up-to-date and complete information on image fusion algorithms, design architectures and applications. *Brings together ...
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.