Integrating Visual Context and Object Detection within a Probabilistic Framework

  • Authors:

  • Roland Perko;Christian Wojek;Bernt Schiele;Aleš Leonardis

  • Affiliations:

  • University of Ljubljana, Slovenia;TU Darmstadt, Germany;TU Darmstadt, Germany;University of Ljubljana, Slovenia

  • Venue:
  • Attention in Cognitive Systems
  • Year:
  • 2009

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Abstract

Visual context provides cues about an object's presence, position and size within an observed scene, which are used to increase the performance of object detection techniques. However, state-of-the-art methods for context aware object detection could decrease the initial performance. We discuss the reasons for failure and propose a concept that overcomes these limitations, by introducing a novel technique for integrating visual context and object detection. Therefore, we apply the prior probability function of an object detector, that maps the detector's output to probabilities. Together, with an appropriate contextual weighting, a probabilistic framework is established. In addition, we present an extension to state-of-the-art methods to learn scale-dependent visual context information and show how this increases the initial performance. The standard methods and our proposed extensions are compared on a novel, demanding image data set. Results show that visual context facilitates object detection methods.