Work

CrowdMOT

Year: 2021

Type: article

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.

Source: Proceedings of the ACM on Human-Computer Interaction

DOI: 10.1145/3434175

Authors: Samreen Anjum, Chi Lin, Danna Gurari

Institution: The University of Texas at Austin

Language: English

Topic: Mobile Crowdsensing and Crowdsourcing

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Silicon Valley Community Foundation, Chan Zuckerberg Initiative

Award: G4697112860