Abstract

Accurately annotating entities in video is labor intensive and expensive. As the quantity of online video grows, traditional solutions to this task are unable to scale to meet the needs of researchers with lim- ited budgets. Current practice provides a temporary solution by paying dedicated workers to label a fraction of the total frames and otherwise settling for linear interpolation. As budgets and scale require sparser key frames, the assumption of linearity fails and labels become inaccurate. To address this problem we have created a public framework for dividing the work of labeling video data into micro-tasks that can be completed by huge labor pools available through crowdsourced marketplaces. By extracting pixel-based features from manually labeled entities, we are able to leverage more sophisticated interpolation between key frames to maximize performance given a budget. Finally, by validating the power of our framework on difficult, real-world data sets we demonstrate an inherent trade-off between the mix of human and cloud computing used vs. the accuracy and cost of the labeling.