Cognitive visual tracking and camera control

Authors:
Nicola Bellotto;Ben Benfold;Hanno Harland;Hans-Hellmut Nagel;Nicola Pirlo;Ian Reid;Eric Sommerlade;Chuan Zhao
Affiliations:
School of Computer Science, University of Lincoln, UK;Department of Engineering Science, University of Oxford, UK;Institut für Algorithmen und Kognitive Systeme, Karlsruhe Institute of Technology, Germany;Institut für Algorithmen und Kognitive Systeme, Karlsruhe Institute of Technology, Germany;Institut für Algorithmen und Kognitive Systeme, Karlsruhe Institute of Technology, Germany;Department of Engineering Science, University of Oxford, UK;Department of Engineering Science, University of Oxford, UK;Department of Engineering Science, University of Oxford, UK
Venue:
Computer Vision and Image Understanding
Year:
2012

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Abstract

Cognitive visual tracking is the process of observing and understanding the behavior of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision.