Speeded up detection of squared fiducial markers

By Francisco José Romero-Ramírez, Rafael Muñoz-Salinas, Rafael Medina Carnicer. Image and Vision Computing, 2018

646 citations recorded by the source corpus.

Abstract Squared planar markers have become a popular method for pose estimation in applications such as autonomous robots, unmanned vehicles and virtual trainers. The markers allow estimating the position of a monocular camera with minimal cost, high robustness, and speed. One only needs to create markers with a regular printer, place them in the desired environment so as to cover the working area, and then registering their location from a set of images. Nevertheless, marker detection is a time-consuming process, especially as the image dimensions grows. Modern cameras are able to acquire high resolutions images, but fiducial marker systems are not adapted in terms of computing speed. This paper proposes a multi-scale strategy for speeding up marker detection in video sequences by wisely selecting the most appropriate scale for detection, identification and corner estimation. The experiments conducted show that the proposed approach outperforms the state-of-the-art methods without sacrificing accuracy or robustness. Our method is up to 40 times faster than the state-of-the-art method, achieving over 1000 fps in 4 K images without any parallelization.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f6166cda97b51aff48c95097b501859b815cfe1b

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In Aerospace Engineering

  1. Ben Mildenhall
  2. Matthew Tancik

In Computer Vision and Pattern Recognition

  1. Ben Mildenhall
  2. Matthew Tancik
  3. Ren Ng
  4. Jonathan T. Barron
  5. Andreas Geiger
  6. Pratul P. Srinivasan
  7. Luc Van Gool
  8. Chen Change Loy

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