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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 pingpong. The corpus consists of ping pong play with three main annotation streamsthat can be used to learn tracking and action recognition models -- tracking ofthe ping pong ball and poses of humans in the videos and the spin of the ballbeing hit by humans. The training corpus consists of 53 hours of data withlabels derived from previous models in a semi-supervised method. The testingcorpus contains 1 hour of data with the same information, except that crowdcompute was used to obtain human annotations of the ball position, from whichball spin has been derived. Along with the dataset we introduce severalbaseline models that were trained on this data. The models were specificallychosen to be able to perform inference at the same rate as the images aregenerated -- specifically 150 fps. We explore the advantages of multi-tasktraining on this data, and also show interesting properties of ping pong balltrajectories that are derived from our observational data, rather than fromprior physics models. To our knowledge this is the first large scale dataset ofping pong; we offer it to the community as a rich dataset that can be used fora large variety of machine learning and vision tasks such as tracking, poseestimation, semi-supervised and unsupervised learning and generative modeling.

SPIN: A High Speed, High Resolution Vision Dataset for Tracking and Action Recognition in Ping Pong