Current Research/Research Interests

I am currently researching the modelling of `object behaviours' using detailed, learnt statistical models. The techniques being developed will allow models of characteristic object behaviours to be learnt from the continuous observation of long image sequences.

It is hoped that these models of characteristic behaviours will have a number of uses, particularly in automated surveillance and event recognition, allowing the surveillance problem to be approached from a lower level, without the need for high-level scene/behavioural knowledge. Other possible uses include the random generation of realistic looking object behaviour for use in Virtual Reality, and long-term prediction of object behaviours to aid occlusion reasoning in object tracking.

Pedestrian scene	Pedestrian trajectories

We have developed a model of the probability density functions of both the instantaneous movements and partial trajectories of objects moving within a scene (the figure above shows a typical pedestrian scene (left), and a set of pedestrian trajectories within the scene (right)). The model is learnt in an unsupervised manner by tracking objects over long image sequences, and is based on a combination of a neural network implementing Vector Quantisation and a type of neuron with short-term memory capabilities.

Temporal representation of a trajectory	Trajectories matching the temporal representation

Models of the trajectories of pedestrians have been generated and used to assess the typicality of new trajectories (allowing the identification of `incidents of interest' within the scene), predict future object trajectories, and randomly generate new trajectories. The figure above shows a diagrammatic representation of a model vector representing a partial pedestrian trajectory (left), and the set of pedestrian trajectories which it represents (right). A modified version of the model is currently being developed to learn object interactions.

My research interests also include unsupervised learning and self-organising systems.

MPEG Movies

Typicality assessment 2 [6.9M]

Trajectory prediction [8.6M]

QuickTime Movies

Virtual interaction - face nodding/shaking [12.3M]

Publications

• N. Johnson and D. Hogg. Learning the Distribution of Object Trajectories for Event Recognition. In Proc. British Machine Vision Conference, volume 2, pages 583-592, September 1995.

• N. Johnson and D. Hogg. Learning the distribution of object trajectories for event recognition. Image and Vision Computing, 14(8):609-615, August 1996. Extended version of BMVC95 paper.

• N. Johnson, A. Galata and D. Hogg. The Acquisition and Use of Interaction Behaviour Models. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 866-871, June 1998.

• N. Johnson. Learning Object Behaviour Models. Ph.D. thesis, The University of Leeds, September 1998.

Links

• My Home Page
• SCS Computer Vision Group
• UTCS Neural Nets Research Group