BRIEF PROJECT OVERVIEW
The study of marine ecosystems is vital for understanding environmental effects, such as climate change and the effects of pollution, but is extremely difficult because of the inaccessibility of data. Undersea video data is usable but is tedious to analyse (for both raw video analysis and abstraction over massive sets of observations), and is mainly done by hand or with hand-crafted computational tools. Fish4Knowledge will allow a major increase in the ability to analyse this data: 1) Video analysis will automatically extract information about the observed marine animals which is recorded in an observation database. 2) Interfaces will be designed to allow researchers to formulate and answer higher level questions over that database.
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The project will investigate: information abstraction and storage methods for reducing the massive amount of video data (from 10E+15 pixels to 10E+12 units of information), machine and human vocabularies for describing fish, flexible process architectures to process the data and scientific queries and effective specialised user query interfaces. A combination of computer vision, database storage, workflow and human computer interaction methods will be used to achieve this.
The project will use live video feeds from 10 underwater cameras as a testbed for investigating more generally applicable methods for capture, storage, analysis and querying of multiple video streams. We will collate a public database from 2 years containing video summaries of the observed fish and associated descriptors. Expert web-based interfaces will be developed for use by the marine researchers themselves, allowing unprecedented access to live and previously stored videos, or previously extracted information. The marine researcher interface will also allow easy formulation of new queries. Extensive user community evaluations will be carried out to provide information on the accuracy, ease and speed of retrieval of information.
For a longer overview of the planned project: click here
targets in noisy environments.
interactions between the targets.
fish species by integrating multiple 2D perspectively distorted
views over time.
ontologies to interpret user queries.
ontologies to convert queries into workflow sequences.
and accessing massive amounts of video and RDF data in a timely
of the research in a publically usable web tool.
of a fish database suitable for behavioural and environmental
staff in cross-disciplinary methods (computer vision with database
and workflow scientists, computer scientists with biologists).
Fish4Knowledge is funded by the European
Union Seventh Framework Programme [FP7/2007-2013] under
grant agreement 257024,
addressing Objective ICT-2009.4.3: Intelligent Information Management,
Challenge 4: Digital Libraries and Content. NARL/NCHC is funded
by the Taiwan National Science Council under grant agreement NSC 101-2923-I-492-002-MY2.
The project runs from October 1, 2010 through September 30, 2013.