Action Recognition by 3D Convolutional Network

In this paper we focused on sport action recognition with 3D convolutional network. We executed two different experiments. In the first experiment we distinguished two similar activities: running and walking. The experiment proved that 3D convolutional network is able to learn spatio-temporal featur...

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Bibliographic Details
Published in2018 International Symposium ELMAR pp. 71 - 74
Main Authors Brezovsky, Matus, Sopiak, Dominik, Oravec, Milos
Format Conference Proceeding
LanguageEnglish
Published Croatian Society Electronics in Marine - ELMAR 01.09.2018
Subjects
3D convolution
Action recognition
Computer architecture
Computer vision
Convolution
Convolutional neural network
Sports
Three-dimensional displays
Training
Video sequences
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Summary:In this paper we focused on sport action recognition with 3D convolutional network. We executed two different experiments. In the first experiment we distinguished two similar activities: running and walking. The experiment proved that 3D convolutional network is able to learn spatio-temporal features of video sequence. We compared three different 3D convolutional networks and the best accuracy was achieved by architecture Al reached 85%. The second experiment was performed on subset of UCF101 dataset. We selected 15 activities. Architecture A1 achieved accuracy 80.7%. Our results show that it is possible to achieve relatively high accuracy using subtile architecture of 3D convolutional network.
ISBN:9531842442
9789531842440
DOI:10.23919/ELMAR.2018.8534657