Automatic classification of human body postures based on curvelet transform

Abstract

This paper presents the design and implementation of a posture classification method. A new feature extraction strategy according to curvelet transform is provided for identifying the posture in images. First of all, human body is segmented. For this purpose, a background subtraction technique is applied. Then, a curvelet transform is used for extracting features from the posture image. To address the rotation invariance problem, five ratios are evaluated from the human body and they are also included in the set of features. Finally the human body postures are classified through support vector machines (SVM). Experimental results are obtained on the “Fall Detection” dataset. For evaluation, different state of the art statistical measures have been considered such as overall accuracy, the kappa coefficient, the F-measure coefficient, and the area under ROC curve (AUC) value. All of these evaluation measures demonstrate that the proposed approach provides a significant recognition rate.