CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos

Crowdsourcing is a valuable approach for tracking objects in videos in a more scalable manner than possible with domain experts. However, existing frameworks do not produce high quality results with non-expert crowdworkers, especially for scenarios where objects split. To address this shortcoming, we introduce a crowdsourcing platform called CrowdMOT, and investigate two micro-task design decisions: (1) whether to decompose the task so that each worker is in charge of annotating all objects in a sub-segment of the video versus annotating a single object across the entire video, and (2) whether to show annotations from previous workers to the next individuals working on the task. Our experiments highlight strategies for efficiently collecting higher quality annotations than observed using today's state-of-art crowdsourcing system for a greater diversity of videos, which show both familiar objects (aka - people) and unfamiliar objects (aka - cells).