automatic video processing for the EcoGrid

This poses many challenges as there is a vast amount of raw data that
needs to be analysed effectively and efficiently. Furthermore, ecological
data are subject to environmental changes and are exception-prone, hence
their qualities vary. As manual processing by humans can be time and
labour intensive, video processing tools can go some way to addressing
such problems since they are computationally fast. However, most video
analyses that utilise a combination of these tools are still done manually.
We propose a semantic-based hybrid workflow composition method that
strives to provide automation to speed up this process. The requirements
for such a system are presented, whereby we aim for a solution that best
satisfies these requirements and that overcomes the limitations of existing Grid workflow systems that lack automation in their composition.
This hybrid method uses Planning technology to decompose the video
processing tasks, Case Based Reasoning to assist with performance-based
selection and ontologies to provide contexts for the goals, domain description and IP tools available. Ideally, we aim to provide an approximate IP
solution for naive image processing users, for instance video classification
and fish detection in the ecological domain.

http://homepages.inf.ed.ac.uk/gnadaraj/papers/phdProposal.pdf