As a deep learning method for extracting video spatio-temporal features, pseudo-3D residual net(P3D ResNet) uses the objective function of SVM to drive the learning of deep network. In this way, this method inherits the insufficiency of SVM, which only considers the interval between different categories, and ignores the distribution information of similar samples. Aiming at this problem, this paper proposed an improved method called P3D ResNet based on minimum intra-class variance. This method not only embodied the principle of large interval, but also used the distribution information of sample data. Firstly, the method used the feature vector extracted by P3D ResNet to calculate the intra-class divergence matrix. Then it used the matrix to construct a new objective function. Finally, it drove the learning of P3D ResNet by the newly constructed objective function. This paper applied the method to the field of behavior recognition. Experimental results on multiple datasets show that compared with the traditional P3D ResNet, the proposed method achieves higher recognition accuracy and shows better generalization performance.

[2021-12-05] Printed Article /doc/2021.04.0129/v2