论文标题
CrowdMot：在视频中跟踪多个对象的众包策略
CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos
论文作者
论文摘要
众包是一种有价值的方法，用于以更可扩展的方式跟踪对象，而不是域专家。但是，现有的框架不会与非专家人群工人一起产生高质量的结果，尤其是对于对象分裂的情况。为了解决这一缺点，我们介绍了一个称为CrowdMot的众包平台，并研究了两个微任务设计决策：（1）是否要分解任务，以便每个工人负责在视频的子段中注释所有对象，而不是在整个视频中注释单个对象，并在整个视频中向单个对象进行注释，以及（2）（2）是否向下一个工作人员展示了工作人员的工作人员，该对象是从下一项工作的人来工作。我们对各种视频进行实验，这些视频既显示熟悉的对象（又称人）和陌生的对象（又称细胞）。我们的结果强调了使用当今最先进的众包系统采用的策略时观察到的有效收集更高质量注释的策略。
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.
