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- W3119396809 abstract "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. We conduct experiments on a diversity of videos which show both familiar objects (aka - people) and unfamiliar objects (aka - cells). Our results highlight strategies for efficiently collecting higher quality annotations than observed when using strategies employed by today's state-of-art crowdsourcing system." @default.
- W3119396809 created "2021-01-18" @default.
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- W3119396809 date "2021-01-05" @default.
- W3119396809 modified "2023-10-05" @default.
- W3119396809 title "CrowdMOT" @default.
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- W3119396809 doi "https://doi.org/10.1145/3434175" @default.
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