Recognition of Human Action Using Zernike Moment-based Features

To ensure the validity and completeness of feature extraction, a new method of recognition of human action using Zernike moments-based features is introduced. In the proposed method, normalized motion history image for motion representation is valued. Statistical descriptions are then computed from motion history image using Zernike moment-based features for the following recognition. A systematic reconstruction-based method for deciding the highest order of Zernike moments required in a classification problem is developed. Experiments are conducted using instances of eight human actions(i. e. eight classes) performed by different subjects. Experiment results show that Zernike moment features for the recognition of human action are superior to regular moments and Hu monents in the accuracy of classification.