Automatic indexing of underwater survey video: Algorithm and benchmarking method

Katia Lebart, Chris Smith, Emanuele Trucco, David M. Lane

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


It is often the case that only a few sparse sequences of long videos from scientific underwater surveys actually contain important information for the expert. Locating such sequences is time consuming and tedious. A system that automatically detects those critical parts, online or during post-mission tape analysis, would alleviate the expert workload and improve data exploitation. In this paper, a methodology for evaluating the performance of such a system on real data is presented. Interesting sequences are started by changes of visual context. An algorithm to detect significant context changes in benthic videos in real time has been presented by Lebart et al. in 2000. It is used as an illustration for this methodology - its performance is studied and benchmarked on real underwater data, ground truthed by an expert biologist. Various issues relating to the complexity of the problems of automatically analyzing underwater video are also discussed.

Original languageEnglish
Pages (from-to)673-686
Number of pages14
JournalIEEE Journal of Oceanic Engineering
Issue number4
Publication statusPublished - Oct 2003


  • Change detection
  • Image analysis
  • Indexing
  • Light compensation
  • Online image processing
  • Underwater
  • Unsupervised clustering
  • Video


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