Crowdsourcing more effective initializations for single-target trackers through automatic re-querying

Stephan J. Lemmer, Jean Y. Song, Jason J. Corso

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In single-target video object tracking, an initial bounding box is drawn around a target object and propagated through a video. When this bounding box is provided by a careful human expert, it is expected to yield strong overall tracking performance that can be mimicked at scale by novice crowd workers with the help of advanced quality control methods. However, we show through an investigation of 900 crowdsourced initializations that such quality control strategies are inadequate for this task in two major ways: first, the high level of redundancy in these methods (e.g., averaging multiple responses to reduce error) is unnecessary, as 23% of crowdsourced initializations perform just as well as the gold-standard initialization. Second, even nearly perfect initializations can lead to degraded long-term performance due to the complexity of object tracking. Considering these findings, we evaluate novel approaches for automatically selecting bounding boxes to re-query, and introduce Smart Replacement, an efficient method that decides whether to use the crowdsourced replacement initialization.

Original languageEnglish
Title of host publicationCHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Subtitle of host publicationMaking Waves, Combining Strengths
ISBN (Electronic)9781450380966
DOIs
StatePublished - 6 May 2021
Event2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021 - Virtual, Online, Japan
Duration: 8 May 202113 May 2021

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021
Country/TerritoryJapan
CityVirtual, Online
Period8/05/2113/05/21

Keywords

  • Crowd-ai collaboration
  • Crowdsourcing
  • Seed rejection
  • Single-target video object tracking
  • Smart replacement

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