HUMAN ACTION RECOGNITION USING A NEW SPARSE CODING APPROACH

Abstract

Human action recognition (HAR) is a challenging problem because of the complexity and similarity in different actions. In recent years, many methods have been proposed for HAR. Sparse coding-based approaches have been widely used in this field. Also, many works have been done based on manifold learning theory. When the videos are similar but from different classes, their sparse codes may be similar and the actions might be classified mistakenly. In this paper, a multi-modal affine graph regularized sparse coding approach is proposed for solving this problem in HAR. At first, HOG3D, HOG/Hof and SURF3D descriptors were extracted from the action datasets, then the sparse codes have been obtained for each descriptor using the proposed method. The dictionary learning method used in this step has more discrimination power in respect to the traditional methods. Then, these codes are scored differently using SVM classifier and at last a Naïve Bayes leads to a final decision. Experiments on KTH, Weizmann and UCF Sport action datasets show that the proposed method can significantly outperform several previous methods in human action classification especially in real-world data.