Moving cast shadow detection and removal for visual traffic surveillance

  • Authors:

  • Jeong-Hoon Cho;Tae-Gyun Kwon;Dae-Geun Jang;Chan-Sik Hwang

  • Affiliations:

  • School of Electronics & Electrical Engineering, Kyungpook National University, Daegu, Korea;School of Electronics & Electrical Engineering, Kyungpook National University, Daegu, Korea;School of Electronics & Electrical Engineering, Kyungpook National University, Daegu, Korea;School of Electronics & Electrical Engineering, Kyungpook National University, Daegu, Korea

  • Venue:
  • AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
  • Year:
  • 2005

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Abstract

Shadow detection and removal is important to deal with traffic image sequences. The shadow cast by a vehicle can lead to inaccurate object feature extraction and an erroneous scene analysis. Furthermore, separate vehicles can be connected through a shadow, thereby confusing an object recognition system. Accordingly, this paper proposes a robust method for detecting and removing an active cast shadow from monocular color image sequences. A background subtraction method is used to extract moving blobs in color and gradient dimensions, and YCrCb color space adopted to detect and remove the cast shadow. Even when shadows link different vehicles, each vehicle figure can be separately detected using a modified mask based on a shadow bar. Experimental results from town scenes demonstrate that the proposed method is effective and the classification accuracy is sufficient for general vehicle type classification.