publication . Conference object . 2018

Deep 3D Flow Features for Human Action Recognition

Petros Daras; Athanasios Psaltis; Georgios Th. Papadopoulos;
Open Access
  • Published: 01 Nov 2018
  • Publisher: IEEE
The present work investigates the use of 3D flow information for performing Deep Learning (DL)-based human action recognition. Generally, 3D flow fields include rich and fine-grained information, regarding the motion dynamics of the observed human actions. However, despite the great potentials present, 3D flow has not been widely used, mainly due to challenges related to the efficient modeling of the flow information and the addressing of the respective computational complexity issues. In this paper, different techniques are investigated for incorporating 3D flow information in DL action recognition schemes. In particular, a novel sequence modeling approach is i...
Persistent Identifiers
free text keywords: Action recognition, 3D flow, Solid modeling, Flow (psychology), Convolutional neural network, Feature extraction, Spatial correlation, Deep learning, Pattern recognition, Artificial intelligence, business.industry, business, Computational complexity theory, Computer science, Convolution
Funded by
Detecting and ANalysing TErrorist-related online contents and financing activities
  • Funder: European Commission (EC)
  • Project Code: 700367
  • Funding stream: H2020 | IA
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