An adaptive focus-of-attention model for video surveillance and monitoring

  • Authors:

  • James W. Davis;Alexander M. Morison;David D. Woods

  • Affiliations:

  • Ohio State University, Department of Computer Science and Engineering, 2015 Neil Avenue, 43210, Columbus, OH, USA;Ohio State University, Department of Computer Science and Engineering, 2015 Neil Avenue, 43210, Columbus, OH, USA;Ohio State University, Cognitive Systems Engineering Laboratory, Institute for Ergonomics, 2015 Neil Avenue, 43210, Columbus, OH, USA

  • Venue:
  • Machine Vision and Applications
  • Year:
  • 2007

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Abstract

In current video surveillance systems, commercial pan/tilt/zoom (PTZ) cameras typically provide naive (or no) automatic scanning functionality to move a camera across its complete viewable field. However, the lack of scene-specific information inherently handicaps these scanning algorithms. We address this issue by automatically building an adaptive, focus-of-attention, scene-specific model using standard PTZ camera hardware. The adaptive model is constructed by first detecting local human activity (i.e., any translating object with a specific temporal signature) at discrete locations across a PTZ camera’s entire viewable field. The temporal signature of translating objects is extracted using motion history images (MHIs) and an original, efficient algorithm based on an iterative candidacy-classification-reduction process to separate the target motion from noise. The target motion at each location is then quantified and employed in the construction of a global activity map for the camera. We additionally present four new camera scanning algorithms which exploit this activity map to maximize a PTZ camera’s opportunity of observing human activity within the camera’s overall field of view. We expect that these efficient and effective algorithms are implementable within current commercial camera systems.