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.
Highlights
Human activity recognition is an active research area in artificial intelligence, human-computer interaction and computer vision
Digital cameras can record the most daily activities of people and this makes the video sources to be rich on the internet, and brings the problem of video categorization and how a new input video is classified based on their activities classes
UCF50 is an activity recognition data set with 50 www.ijacsa.thesai.org activities classes, composing of real Youtube videos
Summary
Human activity recognition is an active research area in artificial intelligence, human-computer interaction and computer vision. Many researchers engage a lot of attention to these problems They tried to create a machine recognition model which the feature descriptors originated from the training videos are trained to automatically recognize the activities of the new videos [1], [2], [3]. One of the common methods is regularization, which uses in the optimization process of learning in predictive modeling as penalization This approach penalizes the irrelevant features(coefficients) and selects the most important features to reduce the complexity (over-fitting) like LASSO, Ridge regressions. Feature selection in embedded methods performs in the training process of machine learning. It is efficient because no need for splitting data into training and validation sets.
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