Human Action Recognition Algorithm Based on Adaptive Initialization of Deep Learning Model Parameters and Support Vector Machine

Action recognition is a trending topic and key research direction in computer vision, machine learning, artificial intelligence, and other fields. This research seeks to identify human action in image and video data. Its research results have been widely used in the fields of safety monitoring, disability monitoring, understanding multimedia content, human–computer interaction, virtual reality, and so on. However, the existing traditional human action recognition technology has many limitations in practical application, such as low accuracy and weak adaptive ability. Although the action recognition based on deep learning can self-learn and improve the action recognition accuracy, there are many difficulties in training the deep neural network model, such as gradient disappearance, gradient explosion, and overfitting. Therefore, this paper will reduce the abovementioned difficulties in deep neural network model training from the perspective of deep neural network model parameter initialization and then propose a model parameter initialization method based on the multilayer maxout network activation function to solve the difficulties in deep neural network model training. Then, on this basis, a method of learning the temporal and spatial characteristics of human action based on the deep neural network model is proposed. First, the method detects and tracks the human action and uses the restricted Boltzmann machine (RBM) to encode the temporal and spatial features of various parts of the human body. Second, the temporal and spatial feature codes of various parts of the human body are integrated into a global temporal and spatial feature representation method of the action video through a RBM neural network. Finally, the trained SVM classifiers are used to recognize human action. Experiments show that the human action recognition method proposed in this paper not only has high recognition accuracy but also has great adaptability. Thus, this method extracts temporal and spatial features from the shape feature sequences of various parts of the human body, thus opening up a new way to extract human action features and solving the problem of human action recognition in complex scenes. Its proposal provides an exploratory technical method and approach for self-adaptive recognition of human action. It also gives directional enlightenment to the development and improvement of self-adaptive human action methods.