Three-dimensional atrous inception module for crowd behavior classification

Abstract

Abstract 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.