Event detection in underground stations using multiple heterogeneous surveillance cameras

  • Authors:

  • Andrea Cavallaro

  • Affiliations:

  • Multimedia and Vision Lab, Queen Mary, University of London, London, UK

  • Venue:
  • ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
  • Year:
  • 2005

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Abstract

An automatic event detection system is presented that addresses the problem of safety in underground and train stations. The proposed system is based on video analysis from multiple heterogeneous cameras, including sensors in the visible and in the infrared spectrum. Video analysis on surveillance footage from underground stations is a challenging task because of poor image quality, low contrast between pedestrians and the platform, reflections and occlusions. To overcome these problems, statistical analysis, information fusion and domain knowledge are exploited. First, we perform robust object detection in each sensor using statistical colour change detection and a continuously updated background model. Then, we integrate the results using domain knowledge and a common ground plane for all cameras. Finally, a binary decision tree is defined to detect events of interests. The effectiveness of the method is demonstrated on the dataset of the Challenge for Real-time Events Detection Solutions (CREDS).