Optimized selection of key frames for monocular videogrammetric surveying of civil infrastructure

  • Authors:

  • Abbas Rashidi;Fei Dai;Ioannis Brilakis;Patricio Vela

  • Affiliations:

  • School of Civil and Environmental Engineering, Georgia Institute of Technology, 130 Hinman Research Building, 723 Cherry Street NW, Atlanta, GA 30332, United States;Department of Civil and Environmental Engineering, West Virginia University, P.O. Box 6103, Morgantown, WV 26506-6103, United States;Department of Engineering, University of Cambridge, BC2-07, Trumptington Street, Cambridge CB2 1PZ, United Kingdom;School of Electrical and Computer Engineering, Georgia Institute of Technology, TSRB 441/Van Leer 368, Mail Code 0250, Atlanta, GA 30332, United States

  • Venue:
  • Advanced Engineering Informatics
  • Year:
  • 2013

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Abstract

Videogrammetry is an inexpensive and easy-to-use technology for spatial 3D scene recovery. When applied to large scale civil infrastructure scenes, only a small percentage of the collected video frames are required to achieve robust results. However, choosing the right frames requires careful consideration. Videotaping a built infrastructure scene results in large video files filled with blurry, noisy, or redundant frames. This is due to frame rate to camera speed ratios that are often higher than necessary; camera and lens imperfections and limitations that result in imaging noise; and occasional jerky motions of the camera that result in motion blur; all of which can significantly affect the performance of the videogrammetric pipeline. To tackle these issues, this paper proposes a novel method for automating the selection of an optimized number of informative, high quality frames. According to this method, as the first step, blurred frames are removed using the thresholds determined based on a minimum level of frame quality required to obtain robust results. Then, an optimum number of key frames are selected from the remaining frames using the selection criteria devised by the authors. Experimental results show that the proposed method outperforms existing methods in terms of improved 3D reconstruction results, while maintaining the optimum number of extracted frames needed to generate high quality 3D point clouds.