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Published in:
2014 | OriginalPaper | Chapter
Modeling Video Activity with Dynamic Phrases and Its Application to Action Recognition in Tennis Videos
Authors : Jonathan Vainstein, José F. Manera, Pablo Negri, Claudio Delrieux, Ana Maguitman
Published in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Publisher: Springer International Publishing
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We present a novel approach to action recognition in tennis shot sequences. The underlying model considers the per-frame motion to be regarded as a
word
(within an alphabet of possible motions), and the sequence of frames as a
phrase
whose meaning is determined by the words given in a specific order. This feature extraction mechanism allows a semantic treatment of the classification stage using Conditional Random Fields. The system was applied on the RGB videos of the THETIS dataset, achieving an accuracy of over 86% in recognizing 12 different tennis shots among several takes produced by 55 different amateur and professional players.