Abstract: In this paper, we present an approach for regression-based feature selection in human activity recognition. Due to high dimensional features in human activity recognition, the model may have over-fitting and can’t learn parameters well. Moreover, the features are redundant or irrelevant. The goal is to select important discriminating features to recognize the human activities in videos. R-Squared regression criterion can identify the best features based on the ability of a feature to explain the variations in the target class. The features are significantly reduced, nearly by 99.33%, resulting in better classification accuracy. Support Vector Machine with a linear kernel is used to classify the activities. The experiments are tested on UCF50 dataset. The results show that the proposed model significantly outperforms state-of-the-art methods.

Hussein Mazaar, Eid Emary and Hoda Onsi, “Regression-Based Feature Selection on Large Scale Human Activity Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 7(2), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070283

@article{Mazaar2016,
title = {Regression-Based Feature Selection on Large Scale Human Activity Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070283},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070283},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {2},
author = {Hussein Mazaar and Eid Emary and Hoda Onsi}
}