Robust height estimation of moving objects from uncalibrated videos

  • Authors:

  • Jie Shao;Shaohua Kevin Zhou;Rama Chellappa

  • Affiliations:

  • Google Inc., Mountain View, CA and University of Maryland, College Park, MD;Siemens Corporate Research, Princeton, NJ;Center for Automation Research and Department of Electrical and Computer Engineering, University of Maryland, College Park, MD

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2010

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Abstract

This paper presents an approach for video metrology. From videos acquired by an uncalibrated stationary camera, we first recover the vanishing line and the vertical point of the scene based upon tracking moving objects that primarily lie on a ground plane. Using geometric properties of moving objects, a probabilistic model is constructed for simultaneously grouping trajectories and estimating vanishing points. Then we apply a single view mensuration algorithm to each of the frames to obtain height measurements. We finally fuse the multiframe measurements using the least median of squares (LMedS) as a robust cost function and the Robbins-Monro stochastic approximation (RMSA) technique. This method enables less human supervision, more flexibility and improved robustness. From the uncertainty analysis, we conclude that the method with auto-calibration is robust in practice. Results are shown based upon realistic tracking data from a variety of scenes.