Determining the best suited semantic events for cognitive surveillance

C. Fernández, P. Baiget, F. X. Roca, J. Gonzalez

Research output: Contribution to journalArticleResearchpeer-review

19 Citations (Scopus)


State-of-the-art systems on cognitive surveillance identify and describe complex events in selected domains, thus providing end-users with tools to easily access the contents of massive video footage. Nevertheless, as the complexity of events increases in semantics and the types of indoor/outdoor scenarios diversify, it becomes difficult to assess which events describe better the scene, and how to model them at a pixel level to fulfill natural language requests. We present an ontology-based methodology that guides the identification, step-by-step modeling, and generalization of the most relevant events to a specific domain. Our approach considers three steps: (1) end-users provide textual evidence from surveilled video sequences; (2) transcriptions are analyzed top-down to build the knowledge bases for event description; and (3) the obtained models are used to generalize event detection to different image sequences from the surveillance domain. This framework produces user-oriented knowledge that improves on existing advanced interfaces for video indexing and retrieval, by determining the best suited events for video understanding according to end-users. We have conducted experiments with outdoor and indoor scenes showing thefts, chases, and vandalism, demonstrating the feasibility and generalization of this proposal. © 2010 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)4068-4079
JournalExpert Systems with Applications
Publication statusPublished - 1 Apr 2011


  • Advanced user interfaces
  • Cognitive surveillance
  • Content-based video retrieval
  • Event modeling
  • Ontologies


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