We introduce a new high resolution, high frame rate stereo video dataset,
which we call SPIN, for tracking and action recognition in the game of ping
pong. The corpus consists of ping pong play with three main annotation streams
that can be used to learn tracking and action recognition models -- tracking of
the ping pong ball and poses of humans in the videos and the spin of the ball
being hit by humans. The training corpus consists of 53 hours of data with
labels derived from previous models in a semi-supervised method. The testing
corpus contains 1 hour of data with the same information, except that crowd
compute was used to obtain human annotations of the ball position, from which
ball spin has been derived. Along with the dataset we introduce several
baseline models that were trained on this data. The models were specifically
chosen to be able to perform inference at the same rate as the images are
generated -- specifically 150 fps. We explore the advantages of multi-task
training on this data, and also show interesting properties of ping pong ball
trajectories that are derived from our observational data, rather than from
prior physics models. To our knowledge this is the first large scale dataset of
ping pong; we offer it to the community as a rich dataset that can be used for
a large variety of machine learning and vision tasks such as tracking, pose
estimation, semi-supervised and unsupervised learning and generative modeling.

我们介绍了一个新的高分辨率、高帧率的立体视频数据集 SPIN，用于跟踪和动作识别，在乒乓球比赛中进行。数据集包括 53 小时的训练数据和 1 小时的测试数据，并针对该数据集介绍了几个基准模型。该数据集可以用于机器学习和视觉任务的许多领域，如跟踪、姿态估计、半监督和无监督学习以及生成建模。