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A Robust Video-Based Algorithm for Detecting Snow Movement in Traffic Scenes


Video-based Automatic Incident Detection (AID) systems are widely deployed in many cities for detecting traffic incidents to provide smoother, safer and congestion free traffic flow. However, the accuracy of an AID system operating in an outdoor environment suffers from high false alarm rates due to environmental factors. These factors include snow movement, static shadow and static glare on the roads. In this paper, a robust real-time algorithm is proposed to detect snow movement in video streams to improve the rate of detection. This is done by having the AID system reducing its sensitivity in the areas that have snow movements. The feasibility of the proposed algorithm has been evaluated using traffic videos captured from several cameras at the City of Calgary.

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We would like to thank Transport Canada (Transport Canada has provided co-funding to this project, through Canada’s Strategic Highway Infrastructure Program), Alberta Infrastructure and Transportation, The City of Calgary, The University of Calgary, Schulich School of Engineering, and Department of Electrical and Computer Engineering, for their financial and technical support they are providing to us during the period of this research project.

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Correspondence to Mohamed Shehata.

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Cai, J., Shehata, M. & Badawy, W. A Robust Video-Based Algorithm for Detecting Snow Movement in Traffic Scenes. J Sign Process Syst Sign Image Video Technol 56, 307–326 (2009).

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  • Snow movement detection
  • Intelligent transportation systems (ITS)
  • Automatic incident detection (AID)
  • False alarms