Action Recognition by 3D Convolutional Network
Matus Brezovsky, Dominik Sopiak, Miloš Oravec · 2018
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.