Stephan J. Lemmer
University of Michigan, Ann Arbor, Michigan, United States
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 \textit{Smart Replacement}, an efficient method that decides whether to use the crowdsourced replacement initialization.
https://doi.org/10.1145/3411764.3445181
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)