- Abstract:
-
Video data collected continuously are pervasive today but analyzing them in an efficient manner has proven to be a challenge. This is because raw data is unlabelled and prone to noise, causing difficulty in extracting knowledge. With the aid of user-provided domain knowledge and heuristics used by image processing experts, an automated solution is implemented. It makes use of formalisms for goal-directed behavior in the form of hierarchical task networks (HTNs). These are incorporated within a novel workflow composition framework that aims to assist naive users conduct complex video processing tasks automatically. An example is illustrated for video classification, fish detection and fish counting in unconstrained underwater videos.
- Copyright:
- 2010 by The University of Edinburgh. All Rights Reserved
- Links To Paper
- 1st Link
- Bibtex format
- @Misc{EDI-INF-RR-1384,
- author = {
Gayathri Nadarajan
and Jessica Chen-Burger
and Robert Fisher
},
- title = {A Knowledge-Based Planner for Processing Unconstrained Underwater Videos},
- year = 2009,
- month = {Jul},
- url = {http://homepages.inf.ed.ac.uk/s0450937/papers/nadarajan-struck09.pdf},
- note = {IJCAI workshop on Learning Structural Knowledge from Observations (Struck 09)},
- }
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