Action recognition based on binary patterns of action-history and histogram of oriented gradient

Abstract

In this paper, we have focused on the view-based spatio-temporal template matching approach for human action detection and classification. We have proposed an approach for human activity modeling that describes human motions as a texture pattern. We have combined two relatively simple feature extractors for obtaining a system to get more accurate result. In this method, video sequences are converted into temporal templates called Motion History Image (MHI), which can preserve dominant motion information. The local features are described with Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) descriptors. LBP operator is texture operator that encodes the direction of motion from the non-monotonous areas of MHI images. HOG is used as feature descriptor and extracts the features from LBP. These descriptors are used to train with Support Vector Machine (SVM) classifier to recognize various action classes. This proposed method has been tested on the KTH Action Dataset (which is one of the most widely used benchmark datasets for human action classification), and on the Pedestrian Action Dataset. Our method has shown 86.67 % recognition rate in the 6-classes of KTH Action Dataset and 94.3 % accuracy in the 7-classes of Pedestrian Action Dataset. Based on the complexity of datasets, both the results are quite satisfactory.