
PhD (Queensland University of Technology), Bachelor of Information Technology (Queensland University of Technology), Bachelor of Engineering (Electronics) (Queensland University of Technology)
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.
Projects
- Automated Video Endoscopy Data Analysis
- Crowd Monitoring Using Computer Vision
- Handheld 3D Thermography using Range Sensing and Computer Vision
- Human Action Anticipation
- Human Identification in Video Surveillance Using Improved Gait Recognition Approaches
- Person Re-identification using Soft-Biometrics
- Trajectory Based Human Behaviour Understanding
- Unusual Event Detection in Crowded Scenes
Additional information
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
- 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