Human Object Classification Using Dual Tree Complex Wavelet Transform and Zernike Moment

Presence of variety of objects degrade the performance of video surveillance system as a certain type of objects can be misclassified as some other types of object. Recent researches in video surveillance are focused on accurate classification of human objects. Classification of human objects is a crucial problem, as accurate human object classification is a desirable task for better performance of video surveillance system. In this paper we have proposed a method for human object classification, which classify the objects present in a scene into two classes: human and non-human. The proposed method uses combination of Dual tree complex wavelet transform and Zernike moment as feature of object. We have used support vector machine (SVM) as a classifier for classification of objects. The proposed method has been tested on standard dataset like INRIA person dataset. Quantitative experimental results shows that the proposed method is better than other state-of-the-art methods and gives better performance for human object classification.