Human action recognition using bag of global and local Zernike moment features

Human action recognition is a fundamental and challenging building block for many computer vision applications. It has been included in many applications such as: video surveillance, human computer interaction and multimedia retrieval systems. Various approaches have been proposed to solve human action recognition problem. Among others, moment-based methods considered as one of the most simple and successful approach. However, moment-based methods take into consideration only global features while neglect the discriminative properties of local features. In this paper, we propose a new efficient method which combine both Global and Local Zernike Moment (GLZM) features based on Bag-of-Features (BoF) technique. Since using only global features are not sufficient to discriminate similar actions like running, walking and jogging, augmenting these features with localized features helps to improve the recognition accuracy. The proposed method first calculate local temporal Motion Energy Images (MEI) by accumulating frame differences of short time consecutive frames. Then, global and local features are calculated using Zernike moments with different polynomial orders to represent global and local motion patterns respectively. Global features are calculated from the whole region of the human performing action while local features focused on localized regions of the human in order to represent local motion information. Both local and global features are preprocessed using whitening transformation, then bag-of-features algorithm is employed to combine those pool of features and represent each action using new GLZM feature descriptor. Finally, we use multi-class Support Vector Machine (SVM) classifier to recognize human actions. In order to validate the proposed method, we perform a set of experiments using three publicly available datasets: Weizmann, KTH and UCF sports action. Experimental results using leave-one-out strategy show that proposed method achieves promising results compared with other state-of-the-art methods.