@misc{14023,
  author = {Michael Riegler and Duc-Tien Dang-Nguyen and B{\r a}rd Winther and Carsten Griwodz and Konstantin Pogorelov and P{\r a}l Halvorsen},
  title = {Heimdallr: a dataset for sport analysis},
  abstract = {In this paper, we present Heimdallr, a dataset that aims to serve two different purposes. The first purpose is action recognition and pose estimation, which requires a dataset of annotated sequences of athlete skeletons. We employed a crowdsourcing platform where people around the world were asked to annotate frames and obtained more than 3000 fully annotated frames for 42 different sequences with a variety of poses and actions. The second purpose is an improved understanding of crowdworkers, and for this purpose, we collected over 10000 written feedbacks from 592 crowdworkers. This is valuable information for crowdsourcing researchers who explore algorithms for worker quality assessment. In addition to the complete dataset, we also provide the code for the application that has been used to collect the data as an open source software.},
  year = {2016},
  journal = {ACM Multimedia System},
  month = {05/2016},
  publisher = {ACM},
  doi = {http://dl.acm.org/citation.cfm?doid=2910017.2910621},
}