Computer Vision
Our recent work has focused on activity analysis from video, with fundamental research on categorisation, tracking, segmentation and motion modelling, through to the application of this research in several areas. Part of the work is exploring the integration of vision within a broader cognitive framework that includes audition, language, action, and reasoning. Details of our past work and alumni can be found here.
Who we are
David Hogg
Professor of Artificial Intelligence, Lab Director
Andy Bulpitt
Professor of Computer Science
Anthony (Tony) Cohn
Professor of Automated Reasoning
Rebecca Stone
PhD Research Student
Mohammed Alghamdi
PhD Research Student
Caitlin Howarth
PhD Research Student
Jose Sosa Martinez
PhD Research Student
Fangjun Li
PhD Research Student
Alumni
Research summaries
Re-animating Characters from TV Shows
Learning appearance and language characteristics from TV shows for re-animating a talking head
Activity monitoring and recovery
Learning and predicting activities in an egocentric setup, applied to equipment workflow
Activity Learning
Learning about the activities within a scene, and the objects involved in these activities
Seeing to learn
Observational learning of robotic manipulation tasks
Tracking carried objects
Carried object detection is applied using geometric shape properties and tracking is performed using spatio-temporal consistency between the object and the person
Unsupervised activity analysis
Learning about activities observed from a mobile robot
Facial animation
Synthesise an interactive agent by learning from the interactive behaviour of people
Bicycle theft detection
Resolving visual ambiguity by finding consistent explanations, applied to the detection of theft from bicycle racks
Carried bag detection
Detecting large objects (e.g. bags) carried by pedestrians from video
Learning table-top games
Learn about the objects and patterns of moves used in simple table-top games, and then apply these to play the game
Modelling pedestrian intentions
Detecting atypical pedestrian pathways, assuming a simple model of goal-directed navigational behaviou
Consistent tracking
Enforcing global spatio-temporal consistency to enhance reliability of moving object tracking and classification
Traffic interaction
Modelling traffic interaction using learnt qualitative spatio-temporal relations and variable length Markov models
Vehicle theft
Detecting unusual events by modelling simple interactions between people and vehicles
Anomaly detection in video
Motion representations learning using a convolutional autoencoder with a sparsity constraint; and normality modelling and anomalies detection using one-class SVMs.