Page 205 - DCAP208_Management Support Systems

Management Support Systems

Notes “visually guided” 2-dimensional autonomous virtual agents. Both the pursuer and the evader
have limited amounts of energy, which is used up in movement, so they have to evolve to move
economically. Each contest results in a time-series of position and orientation data for the two
agents.
These time-series are then fed into a custom 3-D movie generator.

Notes It is important to note that, although the chase behaviors are genuine data, the 3D
structures, surface physics, and shading are all purely for illustrative effect.

12.2.3 Learning the Distribution of Object Trajectories for Event
Recognition

This research work is about 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.

Figure 12.7: Learning mode Figure 12.8: Predict Mode

Source: http://tralvex.com/pub/nap/#CoEvolution of Neural Networks for Control of Pursuit & Evasion
In figure 12.7, 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
Quantization and a type of neuron with short-term memory capabilities.
In figure 12.8, 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.

12.2.4 Radiosity for Virtual Reality Systems (ROVER)

The synthesis of actual and computer generated photo-realistic images has been the aim of
artists and graphic designers for many decades. Some of the most realistic images were generated
using radiosity techniques. Unlike ray tracing, radiosity models the actual interaction between
the lights and the environment. In photo realistic Virtual Reality (VR) environments, the need
for quick feedback based on user actions is crucial.

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