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Hierarchical database for a multi-camera surveillance system

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Abstract

This paper presents a framework for event detection and video content analysis for visual surveillance applications. The system is able to coordinate the tracking of objects between multiple camera views, which may be overlapping or non-overlapping. The key novelty of our approach is that we can automatically learn a semantic scene model for a surveillance region, and have defined data models to support the storage of tracking data with different layers of abstraction into a surveillance database. The surveillance database provides a mechanism to generate video content summaries of objects detected by the system across the entire surveillance region in terms of the semantic scene model. In addition, the surveillance database supports spatio-temporal queries, which can be applied for event detection and notification applications.

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Acknowledgements

We would like to acknowledge support from the UK Engineering and Physical Science Research Council (EPSRC) under grant number GR/M58030. Thanks also to Ming Xu.

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Authors and Affiliations

  1. Digital Imaging Research Centre, Kingston University, Kingston-upon-Thames, Surrey, KT1 2EE, UK

    James Black, Dimitrios Makris & Tim Ellis

Authors
  1. James Black
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  2. Dimitrios Makris
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  3. Tim Ellis
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Corresponding author

Correspondence to Dimitrios Makris.

Appendices

Appendix

Biographies

Dr James Black received the BEng and PhD degrees in computer systems engineering and information engineering in 1994 and 2004, respectively, from City University. He has worked both in industry and academia. His research has primarily focussed on multi-view image surveillance using a combination of motion and colour cues, and performance evaluation of video-tracking algorithms. His research interests are in computer vision, pattern recognition and machine learning. He is an associate member of the IEEE computer society.

Dr Dimitrios Makris received his first degree (Diploma) in Electrical and Computer Engineering from Aristotle University of Thessaloniki in 1999 and his PhD in Computer Vision from City University, London in 2004. He joined the School of Computing and Information Systems at Kingston University as a research assistant in October 2003 and he was promoted to lecturer in August 2004. His research interests are in the area of image processing, computer vision and machine learning. His experience is mainly in the area of motion and activity analysis, where he worked on 3D vision and on automated visual surveillance. He is a member of the Institute of Electrical& Electronic Engineers (IEEE), the Institute of Electrical Engineers (IEE) and the British Machine Vision Association (BMVA).

Professor Tim Ellis was appointed professor and Head of School of Computing and Information Systems at Kingston University in August 2003, where he is a member of the Digital Imaging Research Centre (DIRC). His research interests include: multi-camera video surveillance, real-time vision hardware, feature extraction in complex environments, colour-based object recognition, object tracking, real-time implementation of algorithms and the use of multiple cameras for extracting 3D measurements. His research has been funded by: an SERC Advanced Fellowship in Intelligent Instrumentation (1984–1999); the Scientific Research and Development Branch of the Home Office for a project in security surveillance (1986–1988); EPSRC IMCASM—Intelligent Multi-Camera Surveillance and Monitoring (GR/GR/M58030) and Hybrid Vision and Active Sensor Approach to Monitoring and Inspection Applications (GR/H41898), the Royal Society (1995–1997) and Electricite de France (1988–1991). He has been a member of the Executive Committee of the British Machine Vision Association since 1991 and was Chairman from 1997–2000. He was Co-Chair for BMVC2004 at Kingston University.

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Black, J., Makris, D. & Ellis, T. Hierarchical database for a multi-camera surveillance system. Pattern Anal Applic 7, 430–446 (2004). https://doi.org/10.1007/s10044-005-0243-8

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