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Pose-Projected Action Recognition Hourglass Network (PARHN) in Soccer

Current research on soccer action recognitiondoes not focus on player-level actions. We introduce thepose-projected action recognition hourglass network (PARHN) for performing player-level action recognition in soccer. Thisnetwork is inspired by ARHN, a network originally introducedfor hockey action recognition. PARHN has two main noveltiesin its structure. First, it includes an embedded pose projectioncomponent that regularizes the numerical range of the player'spose vector, by applying two separate zero-phase componentanalysis (ZCA)-whitening. The projected pose vector is ef-fectively learned by few succeeding layers, and significantlyimproves the performance of the network and its generalizationability. Second, PARHN incorporates the temporal informationby having a parallel structure for extracting projected posevectors from all frames of an input sequence and also byusing Long short-term memory (LSTM) layers to integratethe pose vectors across the input frames. Also, a new dataset, named SAR4 (standing for, Soccer Action Recognition for 4action types), is generated. It includes 1292 video sequences, inwhich soccer players are tracked and labeled for performingfour types of action (i.e., goalkeeper diving, player shooting, receiving pass and giving pass). Introduced network achievesthe overall F1-score of 88.1% on the test data, which is 20.1%better than the result of the baseline network, ARHN.