Dr Simon Denman
Faculty of Engineering,
School of Electrical Engineering & Robotics
Biography
Dr Simon Denman is a Senior Lecturer in the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT). Simon actively researches in the fields of computer vision and machine learning, including action and event recognition, trajectory prediction, video analytics, biometrics, and medical signal processing. Simon has published over 140 papers in the areas of computer vision and machine learning, and co-leads the Applied Data Science research programme within the QUT Centre for Data Science.Personal details
Positions
- Senior Lecturer
Faculty of Engineering,
School of Electrical Engineering & Robotics
Keywords
Machine Learning, Computer Vision, Signal Processing, Deep Learning
Research field
Artificial intelligence, Electrical engineering
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2020
Qualifications
- PhD (Queensland University of Technology)
- Bachelor of Information Technology (Queensland University of Technology)
- Bachelor of Engineering (Electronics) (Queensland University of Technology)
Teaching
- CAB420, Machine Learning, Lecturer and Unit Coordinator (2020 - 2022)
- ENN543, Data Analytics and Optimisation, Lecturer and Unit Coordinator (2019 - 2022)
- EGH404, Research in Engineering Practice, Lecturer (2017 - 2019)
Experience
Research Areas:
- Computer Vision, Machine Learning, Deep Learning, Pattern Recognition
- Video Surveillance, Object Tracking, Crowd Counting, Anomaly Detection, Trajectory Prediction
- Action and Event Recognition, Segmentation and Prediction
- Biometrics, including Face, Gait, and Iris Recognition
Research Applications Include:
- Security and Infrastructure: Monitoring of sites for operations (i.e. reporting crowd sizes and dwell times), and detecting events of interest
- Sports: Predicting behaviours in a sports context, and extracting measures of performance from sports signal data (i.e. video, accelerometer)
- Healthcare: Classifying bio-signals (EGC, EEG, PCG) based on underlying pathologies, detecting events of interest with bio-signals
- Ecology: Detecting species of interest in challenging environments from video data
Publications
- Fernando, T., Fookes, C., Denman, S. & Sridharan, S. (2021). Detection of Fake and Fraudulent Faces via Neural Memory Networks. IEEE Transactions on Information Forensics and Security, 16, 1973–1988. https://eprints.qut.edu.au/210209
- Gammulle, H., Denman, S., Sridharan, S. & Fookes, C. (2020). Fine-grained action segmentation using the semi-supervised action GAN. Pattern Recognition, 98. https://eprints.qut.edu.au/200897
- Gammulle, H., Denman, S., Sridharan, S. & Fookes, C. (2020). Hierarchical Attention Network for Action Segmentation. Pattern Recognition Letters, 131, 442–448. https://eprints.qut.edu.au/200895
- Khatun, A., Denman, S., Sridharan, S. & Fookes, C. (2020). Joint identification-verification for person re-identification: A four stream deep learning approach with improved quartet loss function. Computer Vision and Image Understanding, 197-198. https://eprints.qut.edu.au/201861
- Fernando, T., Denman, S., Ahmedt-Aristizabal, D., Sridharan, S., Laurens, K., Johnston, P. & Fookes, C. (2020). Neural memory plasticity for medical anomaly detection. Neural Networks, 127, 67–81. https://eprints.qut.edu.au/199962
- Zeng, R., Ge, Z., Denman, S., Sridharan, S. & Fookes, C. (2020). Geometry-constrained car recognition using a 3D perspective network. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 1161–1168. https://eprints.qut.edu.au/197063
- Gammulle, P., Denman, S., Sridharan, S. & Fookes, C. (2019). Forecasting Future Action Sequences with Neural Memory Networks. Proceedings of the 30th British Machine Vision Conference 2019, BMVC 201, 1–12. https://eprints.qut.edu.au/200899
- Warnakulasuriya, T., Denman, S., Sridharan, S. & Fookes, C. (2018). Soft + Hardwired attention: An LSTM framework for human trajectory prediction and abnormal event detection. Neural Networks, 108, 466–478. https://eprints.qut.edu.au/126862
- Sridharan, S., Fookes, C., Ryan, D. & Denman, S. (2015). An evaluation of crowd counting methods, features and regression models. Computer Vision and Image Understanding, 130, 1–17. https://eprints.qut.edu.au/75845
- Denman, S., Halstead, M., Fookes, C. & Sridharan, S. (2015). Searching for people using semantic soft biometric descriptions. Pattern Recognition Letters, 68, 306–315. https://eprints.qut.edu.au/85119
QUT ePrints
For more publications by Simon, explore their research in QUT ePrints (our digital repository).
Selected research projects
- Title
- Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP200101942
- Start year
- 2021
- Keywords
Projects listed above are funded by Australian Competitive Grants. Projects funded from other sources are not listed due to confidentiality agreements.