Incremental detection of text on road signs from video with application to a driving assistant system

  • Authors:

  • Wen Wu;Xilin Chen;Jie Yang

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Proceedings of the 12th annual ACM international conference on Multimedia
  • Year:
  • 2004

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Abstract

This paper proposes a fast and robust framework for incrementally detecting text on road signs from natural scene video. The new framework makes two main contributions. First, the framework applies a Divide-and-Conquer strategy to decompose the original task into two sub-tasks, that is, localization of road signs and detection of text. The algorithms for the two sub-tasks are smoothly incorporated into a unified framework through a real time tracking algorithm. Second, the framework provides a novel way for text detection from video by integrating 2D features in each video frame (e.g., color, edges, texture) with 3D information available in a video sequence (e.g., object structure). The feasibility of the proposed framework has been evaluated on the video sequences captured from a moving vehicle. The new framework can be applied to a driving assistant system and other tasks of text detection from video.