Multiple classifier-based spatiotemporal features for living activity prediction

Abstract

Nowadays, the action prediction technique plays an important role in many automatic systems. There are some proposed methods for this issue. However, they retain limitations such as accuracy and computational time, especially for applying in limited resource systems. This paper presents an approach to enhance the efficiency of the activity prediction task. The work processes on multiple classifiers using spatiotemporal features based on scalable feature descriptors, such as histogram of oriented gradients (HOG), histogram of oriented optical flow (HOF), and motion boundary histogram (MBH). In order to improve prediction accuracy, two layers of classified machine models are studied for applying on spatiotemporal features with the dynamic foreground extraction process. The first layer based on unsupervised classification is proposed to construct a dictionary of features, which supports for distinguishing and uniform the number of features. The next task is that supervised machine learning supports for final decision of action classes. The proposed approach was evaluated on several benchmark datasets, which are available online. The results demonstrate that the approach enhances accuracy and efficiency of the prediction system.