Feature regularization and learning for human activity recognition.

Abstract

Feature extraction is an essential component in the design of human activity recognition model. However, relying on extracted features alone for learning often makes the model a suboptimal model. Therefore, this research work seeks to address such potential problem by investigating feature regularization. Feature regularization is used for encapsulating discriminative patterns that are needed for better and efficient model learning. Firstly, a within-class subspace regularization approach is proposed for eigenfeatures extraction and regularization in human activity recognition. In this ap- proach, the within-class subspace is modelled using more eigenvalues from the reliable subspace to obtain a four-parameter modelling scheme. This model enables a better and true estimation of the eigenvalues that are distorted by the small sample size effect. This regularization is done in one piece, thereby avoiding undue complexity of modelling eigenspectrum differently. The whole eigenspace is used for performance evaluation because feature extraction and dimensionality reduction are done at a later stage of the evaluation process. Results show that the proposed approach has better discriminative capacity than several other subspace approaches for human activity recognition. Secondly, with the use of likelihood prior probability, a new regularization scheme that improves the loss function of deep convolutional neural network is proposed. The results obtained from this work demonstrate that a well regularized feature yields better class discrimination in human activity recognition. The major contribution of the thesis is the development of feature extraction strategies for determining discriminative patterns needed for efficient model learning.