On the Development Robust and Fast Algorithm of Action and Identity Recognition

Abstract

Human action recognition and surveillance applications are playing a key important in the present days and took an increasing interest in modern. Since most previous methods strictly limited to action classification in different scenarios and not take attention to human identity that makes an action at the same time. We present a novel and fast algorithm to recognize action and identity in a single framework. We assumed one person makes one action in a video. To identify and training the owner of the video to the classifier, we proposed the watermark embedded as 2-D wavelet transform as binary image, which is contains identity information in the training video. We used these wavelet coefficients as identity descriptors. To represent feature motion representation, we used motion energy image (MEI) and motion history image (MHI) as temporal template of the human actions and Zernike moments to extract shape features of the action from MEI and MHI. In this research, a set of Zernike moment based feature vectors is proposed for human action recognition, which is capture the global properties of an object rather than the local ones. We have composed two different feature vectors by evaluating the variance values of lower order Zernike moments in the four-dimensional Zernike moment space with encouraging experimental results. It has discriminative information that is suitable for classification, especially on related actions, such as running and jogging, that is most previous researches fail to classify them even human vision HVS. Nearest neighbor classifier is used for action and identity categorization. The result of these experiments suggests that this method has a high recognition rate in both action and identity accuracy on KTH data sets.