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Sport Action Recognition with Siamese Spatio-Temporal CNNs: Application to Table Tennis

Abstract : Human action recognition in video is one of the key problems in visual data interpretation. Despite intensive research, the recognition of actions with low inter-class variability remains a challenge. This paper presents a new Siamese Spatio-Temporal Convolutional neural network (SSTC) for this purpose. When applied to table tennis, it is possible to detect and recognize 20 table tennis strokes. The model has been trained on a specific dataset, TTStroke-21, recorded in natural condition (markerless) at the Faculty of Sports of the University of Bordeaux. Our model takes as inputs a RGB image sequence and its computed Optical Flow. After 3 spatio-temporal convolutions, data are fused in a fully connected layer of a proposed siamese network architecture. Our method reaches an accuracy of 91.4% against 43.1% for our baseline.
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https://hal.archives-ouvertes.fr/hal-02360011
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Submitted on : Tuesday, November 12, 2019 - 4:10:41 PM
Last modification on : Tuesday, January 4, 2022 - 5:36:33 AM
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Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri, Julien Morlier. Sport Action Recognition with Siamese Spatio-Temporal CNNs: Application to Table Tennis. 2018 International Conference on Content-Based Multimedia Indexing (CBMI), Sep 2018, La Rochelle, France. pp.1-6, ⟨10.1109/CBMI.2018.8516488⟩. ⟨hal-02360011⟩

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