Understanding fish behavior during typhoon events in real-life underwater environments

Concetto Spampinato, Simone Palazzo, Bastian Boom, Jacco Ossenbruggen, Isaak Kavasidis, Roberto Salvo, Fang-Pang Lin, Daniela Giordano, Lynda Hardman, Robert B Fisher

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1-38
Number of pages38
JournalMultimedia Tools and Applications
Issue number2
Publication statusPublished - 2012


  • Fish detection
  • Behavior understanding
  • Event detection
  • Covariance tracking

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