RESUMEN
Recent advances in deep learning have led to a surge in computer vision research, including the recognition and classification of human behavior in video data. However, most studies have focused on recognizing individual behaviors, whereas recognizing crowd behavior remains a complex problem because of the large number of interactions and similar behaviors among individuals or crowds in video surveillance systems. To solve this problem, we propose a three-dimensional atrous inception module (3D-AIM) network, which is a crowd behavior classification model that uses atrous convolution to explore interactions between individuals or crowds. The 3D-AIM network is a 3D convolutional neural network that can use receptive fields of various sizes to effectively identify specific features that determine crowd behavior. To further improve the accuracy of the 3D-AIM network, we introduced a new loss function called the separation loss function. This loss function focuses the 3D-AIM network more on the features that distinguish one type of crowd behavior from another, thereby enabling a more precise classification. Finally, we demonstrate that the proposed model outperforms existing human behavior classification models in terms of accurately classifying crowd behaviors. These results suggest that the 3D-AIM network with a separation loss function can be valuable for understanding complex crowd behavior in video surveillance systems.
RESUMEN
Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material compositions are key factors for ensuring product quality. In this study, we propose a method for generating the time-series working patterns of the control variables for metal-melting induction furnaces and continuous casting machines, thus improving product quality by aiding machine operators. We used an auxiliary classifier generative adversarial network (AC-GAN) model to generate time-series working patterns of two processes depending on product type and additional material data. To check accuracy, the difference between the generated time-series data of the model and the ground truth data was calculated. Specifically, the proposed model results were compared with those of other deep learning models: multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). It was demonstrated that the proposed model outperformed the other deep learning models. Moreover, the proposed method generated different time-series data for different inputs, whereas the other deep learning models generated the same time-series data.