Combining Zernike moment and complex wavelet transform for human object classification

Abstract

Human object classification is an important problem for smart video surveillance, where we classify human object in real scenes. In this paper, we have proposed a method for human object classification, which classify the object present in a scene into one of the two classes: human and non-human. The proposed method uses combination of Daubechies complex wavelet transform and Zernike moment as a feature of object. The motivation behind using combination of these two as a features of object, because shift-invariance and better edge representation property makes Daubechies complex wavelet transform suitable for locating object, whereas rotation invariance property of Zernike moment is also helpful for correct object identification. We have used Adaboost as a classifier for classification of the objects. The proposed method has been tested on different standard dataset. Quantitative experimental evaluation result shows that the proposed method gives better performance than other state-of-the-art methods for human object classification.