We are investigating how the power of crowd-workers can be leveraged to validate, augment and partition metadata on large quantities of video. Using the Bootlegger platform as a data-store and initial set of context based meta data, we are developing unit task designs that deliver efficient, rich and high quality metadata at a sub-clip level.

Examples include:

  • Focus and audio checking
  • Correction of shot type
  • Tracking individuals through footage
  • Subjecting content testing (crowd editing)
  • Audio description


  • NodeJS
  • Crowdflower API