Project Proposal (PDF) - Oxford Brookes University

FP7-ICT-2011-9 STREP proposal
18/01/12 v1 [Dynact]
v) Risk assessment and contingency plan
Some of the objectives of the proposals, reflected in the above work packages, admittedly involve a certain
degree of risk.
In particular, the development of inference algorithms for imprecise hidden Markov models is cutting edge
research at this time, while the formulation of a general theory for arbitrary imprecise-probabilistic graphical
models poses a beautiful but serious challenge. The same can be said for the development of dynamical
discriminative models, able to exploit the information provided by motion dynamics and couple it with the
state-of-the-art performances of discriminative methods. Finally, the classification of “precise” graphical
models is also subject of current, challenging research: this is in particular true for the theory of
metric/manifold learning for dynamical models. Its extension to imprecise-probabilistic graphical models is
an ambitious goal, which will be crucial to the success of the project.
These risks are balanced by the strong skills of the four academic partners in their respective area of
expertise, and the specific features of the Coordinator with his interdisciplinarity expertise in both the
applications object of this project (action, gesture and identity recognition) and its theoretical side (theory of
imprecise probabilities and classification of dynamical models).
From a general point of view, as we mentioned above, the project is articulated into three different pipelines.
Overall the project is designed as a “pincer movement”, in which different arms are designed to attack the
problem from different angles in a complementary and cooperative manner. While a coherent formulation of
inference and classification algorithms for imprecise-probabilistic graphical models is a challenge that
cannot be met by simply building on existing results, WP4 activities (data gathering and feature extraction)
are characterised by a lower level of risk, while, given OBU's expertise in computer vision applications and
software development, we not envisage any insurmountable difficulty with integration and testing (WP5).
As for WP5's different scenarios, our strong industrial partner Dynamixyz has the necessary background to
guarantee proper validation and testing in the virtual animation context, while OBU has running projects and
extensive expertise in action recognition, gait identification, gaming and entertainment, feature selection and
extraction, autonomous navigation and robotics.
Concerning the consortium's management, three of the four academic partners have smoothly and fruitfully
worked together in recent years without any particular issue: it seems therefore reasonable to expect the same
will happen in the course of this project as well.
To moderate risk, alternative approaches are considered from the start in the different workpackages, in order
to ensure at least one of them will deliver within the desired time frame. For instance, in work package 2 we
have foreseen to pursue several different alternative options to the classification of imprecise Markov and
graphical models, based on clustering, structured kernel learning, and possible compressed sensing.
Further actions will be assessed by the steering committee in its periodic meetings and through informal
contacts and video conferences between the partners. All actions deemed necessary will be proactively
considered and enacted.
Proposal Part B: page [36] of [67]