Modeling Video Activity with Dynamic Phrases and Its Application to Action Recognition in Tennis Videos

JONATHAN VAINSTEIN; JOSÉ F. MANERA; PABLO NEGRI; CLAUDIO DELRIEUX; ANA MAGUITMAN

Puerto Vallarta

Congreso; Iberoamerican Congress on Pattern Recognition; 2014

IAPR

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.