Dr. Xingchen Zhang - Personal website

1 Computer Vision

4.1 Pedestrian intention/motion prediction

This research aims to understand and predict pedestrian intention and motion.

Related publications:

1. X. Zhang*, P. Angeloudis, Y Demiris. ST CrossingPose: A Spatial-Temporal Graph Convolutional Network for Skeleton-based Pedestrian Crossing Intention Prediction. IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 20773-20782, 2022.

2. X. Zhang*, P. Angeloudis, Y. Demiris. Dual-branch Spatio-Temporal Graph Neural Networks for Pedestrian Trajectory Prediction. Pattern Recognition, 2023.

* indicates that I am the corresponding author.

4.2 Video object tracking

This research aims to understand and predict pedestrian intention and motion.

Related publications:

1. J. Liu, G. Xiao, X. Zhang*, P. Ye, X. Xiong, S. Peng. Anti-occlusion Object Tracking Based on Correlation Filter, Signal, Image and Video Processing, vol. 14, no. 4, pp. 753-761, 2020.

2. J. Liu, P. Ye, X. Zhang*, G. Xiao. Real-time long-term tracking with reliability assessment and object recovery. IET Image Processing, accepted.

3. J. Zhao, G. Xiao*, X. Zhang*, D. P. Bavirisetti. An Improved Long-term Correlation Tracking Method with Occlusion Handling. Chinese Optics Letters, vol. 17, no. 3, pp. 031001-1 - 031001-6, 2019.

4.3 Vision-based traffic surveillance system

1. X. Zhang*, Y. Feng, P. Angeloudis, Y Demiris. Monocular Visual Traffic Surveillance: A Review, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 14148-14165, 2022.

2 Deep Learning

2.1 Multimodal learning

2.2 Self-supervised learning

2.3 Graph-based learning

3 Image fusion

The aim of this research is to combine complementary information from multiple images to form a more informative image. This image can help to improve performance in many applications, such as detection and tracking. One of my major objectives is to help create benchmarks in the field of image fusion, which can be utilized to perform fair and comprehensive performance evaluation. My work fills this gap. Besides, I mainly work on deep learning-based image fusion algorithms, including visible-infrared image fusion, multi-focus image fusion, and multi-exposure image fusion.

Related publications:

1. X. Zhang*, Y. Demiris. Visible and Infrared Image Fusion using Deep Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10535-10554, 2023.

2. X. Zhang*. Deep Learning-based Multi-focus Image Fusion: A Survey and A Comparative Study, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 9, pp. 4819-4838.

3. X. Zhang*. Benchmarking and Comparing Multi-exposure Image Fusion Algorithms. Information Fusion, vol. 74, pp. 111-131, 2021.

4. X. Zhang*, P. Ye, G. Xiao. VIFB: A Visible and Infrared Image Fusion Benchmark, In the Proceedings of IEEE/CVF Conference on Computer Vision Workshops, 2020.

5. X. Zhang*. Multi-focus image fusion: A benchmark. arXiv preprint arXiv:2005.01116 (2020).

4 Multimodal perception and its applications (e.g., RGB-T tracking)

The aim of this research is to combine complementary information from visible and infrared images for various applications, e.g., RGB-T tracking.

Related publications:

1. X. Zhang* , Y. Demiris. Self-Supervised RGB-T Tracking with Cross-Input Consistency. arXiv preprint arXiv:2301.11274 (2023).

2. X. Zhang, P. Ye, H. Leung, K. Gong, G. Xiao. Object Fusion Tracking Based on Visible and Infrared Images: A Comprehensive Review, Information Fusion, vol. 63, pp.166-187, 2020.

3. X. Zhang, P. Ye, S. Peng, J. Liu, G. Xiao. DSiamMFT: An RGB-T fusion tracking method via dynamic Siamese networks using multi-layer feature fusion, Signal Processing: Image Communication, vol. 84, 2020.

4. X. Zhang, P. Ye, J. Liu, K. Gong, G. Xiao. Decision-level visible and infrared fusion tracking via Siamese networks. Chinese Journal of Information Fusion, vol.1, pp. 33-41, 2020. (extended version of the conference paper which was awarded the Best Paper Honorable Mention Award of the 9th Chinese Conference on Information Fusion)

5. X. Zhang, P. Ye, S. Peng, J. Liu, K. Gong, G. Xiao. SiamFT: An RGB-infrared Fusion Tracking Method via Fully Convolutional Siamese Networks, IEEE Access, vol. 7, no. 1, pp. 122122 -122133, 2019.

6. X. Zhang, P. Ye, D. Qiao, J. Zhao, S. Peng, G. Xiao. Object Fusion Tracking Based on Visible and Infrared Images Using Fully Convolutional Siamese Networks. In Proceedings of the 22nd International Conference on Information Fusion, 2019.

5 Ethical AI