Human-readable fiducial marker classification using convolutional neural networks

Yanfeng Liu, Eric T. Psota, Lance C. Pérez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many applications require both the location and identity of objects in images and video. Most existing solutions, like QR codes, AprilTags, and ARTags use complex machine-readable fiducial markers with heuristically derived methods for detection and classification. However, in applications where humans are integral to the system and need to be capable of locating objects in the environment, fiducial markers must be human readable. An obvious and convenient choice for human readable fiducial markers are alphanumeric characters (Arabic numbers and English letters). Here, a method for classifying characters using a convolutional neural network (CNN) is presented. The network is trained with a large set of computer generated images of characters where each is subjected to a carefully designed set of augmentations designed to simulate the conditions inherent in video capture. These augmentations include rotation, scaling, shearing, and blur. Results demonstrate that training on large numbers of synthetic images produces a system that works on real images captured by a video camera. The result also reveal that certain characters are generally more reliable and easier to recognize than others, thus the results can be used to intelligently design a human-readable fiducial markers system that avoids confusing characters.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Electro Information Technology, EIT 2017
PublisherIEEE Computer Society
Pages606-610
Number of pages5
ISBN (Electronic)9781509047673
DOIs
StatePublished - Sep 27 2017
Event2017 IEEE International Conference on Electro Information Technology, EIT 2017 - Lincoln, United States
Duration: May 14 2017May 17 2017

Publication series

NameIEEE International Conference on Electro Information Technology
ISSN (Print)2154-0357
ISSN (Electronic)2154-0373

Other

Other2017 IEEE International Conference on Electro Information Technology, EIT 2017
CountryUnited States
CityLincoln
Period5/14/175/17/17

Keywords

  • computer vision
  • convolutional neural network
  • fiducial marker
  • machine learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Human-readable fiducial marker classification using convolutional neural networks'. Together they form a unique fingerprint.

Cite this