查看文章

This paper proposes a real-time human action recognition approach to static video surveillance systems. This approach predicts human actions using temporal images and convolutional neural networks (CNN). CNN is a type of deep learning model that can automatically learn features from training videos. Although the state-of-the-art methods have shown high accuracy, they consume a lot of computational resources. Another problem is that many methods assume that exact knowledge of human positions. Moreover, most of the current methods build complex handcrafted features for specific classifiers. Therefore, these kinds of methods are difficult to apply in real-world applications. In this paper, a novel CNN model based on temporal images and a hierarchical action structure is developed for real-time human action recognition. The hierarchical action structure includes three levels: action layer, motion …