@article{21160,
author       = {Spampinato, C and Palazzo, S. and Boom, B. and van Ossenbruggen, J. R. and Kavasidis, I. and Di Salvo, R. and Lin, F.-P. and Giordano, D. and Hardman, L. and Fisher, R. B.},
title        = {Understanding {Fish} {Behavior} {During} {Typhoon} {Events} {In} {Real-{Life}} {Underwater} {Environments}},
journal      = {Multimedia Tools and Applications},
pages        = {1 - 38},
year         = {2012},
publisher    = {Springer},
issn         = {1380-7501},
refereed     = {y},
size         = {38p.},
group        = {INS2},
scndgroup    = {INS},
language     = {en},
abstract     = {The study of fish populations in their own natural environment is a task
that has usually been tackled in
 invasive ways which inevitably influenced the
behavior of the fish under observation. Recent projects involving the installation
of
 permanent underwater cameras (e.g. the Fish4Knowledge (F4K) project, for the
observation of Taiwan’s coral reefs) allow
 to gather huge quantities of video data,
without interfering with the observed environment, but at the same time require
the
 development of automatic processing tools, since manual analysis would be
impractical for such amounts of videos. Event
 detection is one of the most interesting aspects from the biologists’ point of view, since it allows the analysis of fish
 activity
during particular events, such as typhoons. In order to achieve this goal, in this paper
we present an automatic
 video analysis approach for fish behavior understanding
during typhoon events. The first step of the proposed system, therefore,
 involves
the detection of “typhoon” events and it is based on video texture analysis and on
classification by means
 of Support Vector Machines (SVM). As part of our behavior
understanding efforts, trajectory extraction and clustering have
 been performed to
study the differences in behavior when disruptive events happen. The integration
of event detection
 with fish behavior understanding surpasses the idea of simply
detecting events by low-level features analysis, as it supports
 the full semantic
comprehension of interesting events.},
url          = {http://oai.cwi.nl/oai/asset/21160/21160D.pdf},
}

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B."],"year":2012,"bibtype":"article","biburl":"http://homepages.cwi.nl/~jrvosse/publications/pubs.bib","bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Spampinato"],"firstnames":["C"],"suffixes":[]},{"propositions":[],"lastnames":["Palazzo"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Boom"],"firstnames":["B."],"suffixes":[]},{"propositions":["van"],"lastnames":["Ossenbruggen"],"firstnames":["J.","R."],"suffixes":[]},{"propositions":[],"lastnames":["Kavasidis"],"firstnames":["I."],"suffixes":[]},{"propositions":[],"lastnames":["Di","Salvo"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Lin"],"firstnames":["F.-P."],"suffixes":[]},{"propositions":[],"lastnames":["Giordano"],"firstnames":["D."],"suffixes":[]},{"propositions":[],"lastnames":["Hardman"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Fisher"],"firstnames":["R.","B."],"suffixes":[]}],"title":"Understanding Fish Behavior During Typhoon Events In Real-Life Underwater Environments","journal":"Multimedia Tools and Applications","pages":"1 - 38","year":"2012","publisher":"Springer","issn":"1380-7501","refereed":"y","size":"38p.","group":"INS2","scndgroup":"INS","language":"en","abstract":"The study of fish populations in their own natural environment is a task that has usually been tackled in invasive ways which inevitably influenced the behavior of the fish under observation. Recent projects involving the installation of permanent underwater cameras (e.g. the Fish4Knowledge (F4K) project, for the observation of Taiwan’s coral reefs) allow to gather huge quantities of video data, without interfering with the observed environment, but at the same time require the development of automatic processing tools, since manual analysis would be impractical for such amounts of videos. Event detection is one of the most interesting aspects from the biologists’ point of view, since it allows the analysis of fish activity during particular events, such as typhoons. In order to achieve this goal, in this paper we present an automatic video analysis approach for fish behavior understanding during typhoon events. The first step of the proposed system, therefore, involves the detection of “typhoon” events and it is based on video texture analysis and on classification by means of Support Vector Machines (SVM). As part of our behavior understanding efforts, trajectory extraction and clustering have been performed to study the differences in behavior when disruptive events happen. The integration of event detection with fish behavior understanding surpasses the idea of simply detecting events by low-level features analysis, as it supports the full semantic comprehension of interesting events.","url":"http://oai.cwi.nl/oai/asset/21160/21160D.pdf","bibtex":"@article{21160,\nauthor = {Spampinato, C and Palazzo, S. and Boom, B. and van Ossenbruggen, J. R. and Kavasidis, I. and Di Salvo, R. and Lin, F.-P. and Giordano, D. and Hardman, L. and Fisher, R. B.},\ntitle = {Understanding {Fish} {Behavior} {During} {Typhoon} {Events} {In} {Real-{Life}} {Underwater} {Environments}},\njournal = {Multimedia Tools and Applications},\npages = {1 - 38},\nyear = {2012},\npublisher = {Springer},\nissn = {1380-7501},\nrefereed = {y},\nsize = {38p.},\ngroup = {INS2},\nscndgroup = {INS},\nlanguage = {en},\nabstract = {The study of fish populations in their own natural environment is a task\r\nthat has usually been tackled in\n invasive ways which inevitably influenced the\r\nbehavior of the fish under observation. Recent projects involving the installation\r\nof\n permanent underwater cameras (e.g. the Fish4Knowledge (F4K) project, for the\r\nobservation of Taiwan’s coral reefs) allow\n to gather huge quantities of video data,\r\nwithout interfering with the observed environment, but at the same time require\r\nthe\n development of automatic processing tools, since manual analysis would be\r\nimpractical for such amounts of videos. 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