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SPIN: A High Speed, High Resolution Vision Dataset for Tracking and Action Recognition in Ping Pong | | Steven Schwarcz
; Peng Xu
; David D'Ambrosio
; Juhana Kangaspunta
; Anelia Angelova
; Huong Phan
; Navdeep Jaitly
; | | Date: |
13 Dec 2019 | | Abstract: | 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. | | Source: | arXiv, 1912.6640 | | Services: | Forum | Review | PDF | Favorites | |
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