Research outputs
2025
Identifying Behaviours Indicative of Illegal Fishing Activities in Automatic Identification System Data
Zhou, Y., Davies, R., Wright, J., Ablett, S., & Maskell, S. (2025). Identifying Behaviours Indicative of Illegal Fishing Activities in Automatic Identification System Data. Journal of Marine Science and Engineering, 13(3), 457. doi:10.3390/jmse13030457
2024
Formal verification of robustness and resilience of learning-enabled state estimation systems
Huang, W., Zhou, Y., Jin, G., Sun, Y., Meng, J., Zhang, F., & Huang, X. (2024). Formal verification of robustness and resilience of learning-enabled state estimation systems. Neurocomputing, 585, 127643. doi:10.1016/j.neucom.2024.127643
2023
Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People with Depression
Msosa, Y. J., Grauslys, A., Zhou, Y., Wang, T., Buchan, I., Langan, P., . . . Kehoe, D. (2023). Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People with Depression. IEEE Journal of Biomedical and Health Informatics, 1-12. doi:10.1109/jbhi.2023.3312011
2022
An Automated System to Discover and Track Unknown Geosynchronous Objects Using Ground-based Optical Telescopes
Zhou, Y., Devlin, L., Cook, G., Maskell, S., & Barr, J. (2022). An Automated System to Discover and Track Unknown Geosynchronous Objects Using Ground-based Optical Telescopes. In The 23rd Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference. MAUI, HAWAII. Retrieved from https://amostech.com/TechnicalPapers/2022/Poster/Zhou.pdf
Control Variates for Constrained Variables
Maskell, S., Zhou, Y., & Mira, A. (2022). Control Variates for Constrained Variables. IEEE SIGNAL PROCESSING LETTERS, 29, 2333-2337. doi:10.1109/LSP.2022.3221347
2020
Practical Verification of Neural Network Enabled State Estimation System for Robotics
Huang, W., Zhou, Y., Sun, Y., Sharp, J., Maskell, S., & Huang, X. (2020). Practical Verification of Neural Network Enabled State Estimation System for Robotics. In 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 7336-7343). doi:10.1109/IROS45743.2020.9340720
Robust and Efficient Image Alignment Method Using the Student-t Distribution
Zhou, Y., & Maskell, S. (2020). Robust and Efficient Image Alignment Method Using the Student-t Distribution. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 1255-1262). Retrieved from https://www.webofscience.com/
Reliability Validation of Learning Enabled Vehicle Tracking
Sun, Y., Zhou, Y., Maskell, S., Sharp, J., & Huang, X. (2020). Reliability Validation of Learning Enabled Vehicle Tracking. 2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 9390-9396. doi:10.1109/icra40945.2020.9196932
2019
A Generic Anomaly Detection Approach Applied to Mixture-of-unigrams and Maritime Surveillance Data
Zhou, Y., Wright, J., & Maskell, S. (2019). A Generic Anomaly Detection Approach Applied to Mixture-of-unigrams and Maritime Surveillance Data. In 2019 SYMPOSIUM ON SENSOR DATA FUSION: TRENDS, SOLUTIONS, APPLICATIONS (SDF 2019). doi:10.1109/sdf.2019.8916633
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)
Zhou, Y., & Maskell, S. (2019). Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs). In 2019 22ND INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2019). doi:10.23919/fusion43075.2019.9011271
2018
Analysing Large-scale Surveillance Video
Zhou, Y. (2018, March 7). Analysing Large-scale Surveillance Video.
2017
Moving Object Detection Using Background Subtraction for a Moving Camera with Pronounced Parallax
Zhou, Y., & Maskell, S. (2017). Moving Object Detection Using Background Subtraction for a Moving Camera with Pronounced Parallax. In 2017 SENSOR DATA FUSION: TRENDS, SOLUTIONS, APPLICATIONS (SDF). Retrieved from https://www.webofscience.com/
RB<sup>2</sup>— PF : A novel filter-based monocular visual odometry algorithm
Zhou, Y., & Maskell, S. (2017). RB<sup>2</sup>— PF : A novel filter-based monocular visual odometry algorithm. In 2017 20th International Conference on Information Fusion (Fusion) (pp. 1-8). IEEE. doi:10.23919/icif.2017.8009745
2014
Probabilistic graphical detector fusion for localization of faces and facial parts
Liu, C. Y., Zhou, Y., de Melo, F., & Maskell, S. (2014). Probabilistic graphical detector fusion for localization of faces and facial parts. In 2014 Sensor Data Fusion: Trends, Solutions, Applications (SDF) (pp. 1-6). IEEE. doi:10.1109/sdf.2014.6954708
Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification
Zhang, B., Zhou, Y., Pan, H., & Tillo, T. (2014). Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification. MACHINE VISION AND APPLICATIONS, 25(2), 437-450. doi:10.1007/s00138-013-0588-8
2013
Vehicle Classification with Confidence by Classified Vector Quantization
Bailing Zhang., Yifan Zhou., & Hao Pan. (2013). Vehicle Classification with Confidence by Classified Vector Quantization. IEEE Intelligent Transportation Systems Magazine, 5(3), 8-20. doi:10.1109/mits.2013.2245725
2012
Reliable vehicle type classification by Classified Vector Quantization
Zhang, B., & Zhou, Y. (2012). Reliable vehicle type classification by Classified Vector Quantization. In 2012 5th International Congress on Image and Signal Processing (pp. 1148-1152). IEEE. doi:10.1109/cisp.2012.6469857
VEHICLE TYPE AND MAKE RECOGNITION BY COMBINED FEATURES AND ROTATION FOREST ENSEMBLE
ZHANG, B., & ZHOU, Y. (2012). VEHICLE TYPE AND MAKE RECOGNITION BY COMBINED FEATURES AND ROTATION FOREST ENSEMBLE. International Journal of Pattern Recognition and Artificial Intelligence, 26(03), 1250004. doi:10.1142/s0218001412500048