# GLBRF: Group-Based Lightweight Human Behavior Recognition Framework in Video Camera
## 2 Related Work  
As an essential component of social interaction, human behavior is of great significance for understanding and analyzing human behavior in various fields. With the rapid development of computer vision and machine learning technologies, intelligent machines are beginning to replace humans in observing, recognizing, and analyzing images and video data [8; 9]. Behavior recognition based on computer vision and machine learning is one of these challenges and has become a particularly hot research topic in various fields, such as intelligent monitoring, smart homes, virtual reality, and medical diagnosis. In this section, we discuss some of the related work by other researchers in this area. [PERSON] et al. [10] classified abnormal patient activities by monitoring and recognizing patient activities to recognize activities that require emergency medical assistance. They used YOLO as a backbone CNN model and built a large patient video dataset labeled with patient actions and patient locations to train it. The results showed that the accuracy of abnormal behavior recognition was 96.8%, indicating that the proposed framework can be useful for patient monitoring in hospitals and elderly care facilities. [PERSON] et al. [11] proposed an efficient 2D framework called FENet to significantly reduce the overhead of model storage and computation. To validate the effectiveness of FENet, they conducted several experiments on the UCF101 dataset and showed that FENet achieved optimal accuracy on small datasets and a competitive accuracy of 71.79% with minimal computational cost. However, these studies were based on the 2D CNN structure, which is limited in capturing motion information encoded in multiple consecutive frames because the convolutional filter mainly learns only spatial features and temporal features are reflected only in the last classification layer, which inherently cannot reflect temporal information [12]. Therefore, 3D CNN-based behavior recognition has been proposed to overcome the structural limitations of 2D CNNs. [PERSON] et al. [13] introduced a 3D CNN architecture for classifying human activities in video data, which demonstrated improved precision over existing models. The proposed 3D CNN architecture achieved high test accuracies of 85.2% and 84.4% using the UCF YouTubeAction and UCF101 datasets, demonstrating the effectiveness of 3D CNN in recognizing different activities and offering potential applications in monitoring non-standard human behavior in medical fields and public places by accurately monitoring and classifying activities. [PERSON] et al. [14] presented an algorithm based on double-branch 3D CNN for human behavior classification in videos. It uses two consecutive convolutional neural networks to recognize human actions in videos, extracting temporal and spatial features of video data in the training network and then performing classification and recognition in the test network. Experimental results showed that the algorithm achieved 95.0% accuracy on the UCF-101 dataset. Therefore, 3D CNN is a way to overcome the structural limitations of 2D CNN by adding a time axis to the existing 2D CNN layer configuration and using 3D convolutional operations. Because every convolutional filter is a 3D filter, the feature map generated by a single filter is also three-dimensional. Therefore, 3D CNNs can learn spatiotemporal features for continuous frame data. However, 3D CNNs have the disadvantage of requiringa large amount of computation for training due to large frame datasets and 3D convolutional filters [15]. Previous research has focused on individual behavior recognition rather than group behavior recognition. Although there are prior works on crowd behavior, most of them focus on large crowds and contribute to areas such as crowd counting, tracking, and anomaly detection [16; 17; 18; 19]. In other words, research on behavior recognition in individual small groups is relatively limited.