Vision-Based Human Activity Recognition
Using LDCRFs
Mahmoud
Elmezain1,2 and Ayoub Al-Hamadi3
1Faculty of Science and Computer Engineering, Taibah University, KSA
2Computer Science Division, Faculty
of Science, Tanta University, Egypt
3Institute of Information Technology and Communications,
Otto-Von-Guericke-University, Germany
Abstract: In this paper, an innovative approach for human activity
relies on affine-invariant shape descriptors and motion flow is proposed. The
first phase of this approach is to employ the modelling background that uses an
adaptive Gaussian mixture to distinguish moving foregrounds from their moving
cast shadows. Accordingly, the extracted features are derived from 3D
spatio-temporal action volume like elliptic Fourier, Zernike moments, mass
center and optical flow. Finally, the discriminative model of Latent-dynamic
Conditional Random Fields (LCDRFs) performs the training and testing action
processes using the combined features that conforms vigorous view-invariant
task. Our experiment on an action Weizmann dataset demonstrates that the
proposed approach is robust and more efficient to problematic phenomena than previously
reported. It also can take place with no sacrificing real-time performance for many
practical action applications.
Keywords: Action recognition, Invariant elliptic fourier,
Invariant zernike moments, latent-dynamic conditional random fields.
Received August 15, 2015; accepted January 11, 2016