Determining the best suited semantic events for cognitive surveillance

  • Authors:

  • C. Fernández;P. Baiget;F. X. Roca;J. Gonzílez

  • Affiliations:

  • Computer Vision Center, UAB, Edifici O, Campus UAB, 08193 Barcelona, Spain;Computer Vision Center, UAB, Edifici O, Campus UAB, 08193 Barcelona, Spain;Computer Vision Center, UAB, Edifici O, Campus UAB, 08193 Barcelona, Spain;Computer Vision Center, UAB, Edifici O, Campus UAB, 08193 Barcelona, Spain

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

Quantified Score

Hi-index 12.05

Visualization

Abstract

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.