# GLBRF: Group-Based Lightweight Human Behavior Recognition Framework in Video Camera
###### Abstract  
Behavioral recognition is an important technique for recognizing actions by analyzing human behavior. It is used in various fields, such as anomaly detection and health estimation. For this purpose, deep learning models are used to recognize and classify the features and patterns of each behavior. However, video-based behavior recognition models require a lot of computational power as they are trained using large datasets. Therefore, there is a need for a lightweight learning framework that can efficiently recognize various behaviors. In this paper, we propose a group-based lightweight human behavior recognition framework (GLBRF) that achieves both low computational burden and high accuracy in video-based behavior recognition. The GLBRF system utilizes a relatively small dataset to reduce computational cost using a 2D CNN model and improves behavior recognition accuracy by applying location-based grouping to recognize interaction behaviors between people. This enables efficient recognition of multiple behaviors in various services. With grouping, the accuracy was as high as 98%, while without grouping, the accuracy was relatively low at 68%. behavior recognition; deep learning; video-based; lightweight learning framework; location-based grouping +
Footnote †: journal: Medical  
more suitable for recognizing continuous motion in video data. However, they require large frame datasets and a lot of computation [6]. Recently, there has been a growing demand for intelligent CCTV in smart cities and surveillance systems for social security provisions. Research is not only focused on individuals but also extensively on group behaviors. To recognize group behaviors, identified individuals must be grouped together. Grouping involves analyzing patterns such as size, location, direction, and speed of the recognized individuals and clustering them with similar patterns. For collective behaviors like assaults or fights, classifying the extracted individuals as adjacent groups rather than recognizing individual behaviors can enhance accuracy. This is because it utilizes data that influence the behavior of each member within the group [7]. Developing lightweight models is essential for fast and efficient behavior recognition. In addition, in behavior recognition research, detecting not only individual behaviors but also interaction information between people through grouping is an important factor in improving the accuracy of behavior recognition. Therefore, in this paper, we propose a group-based lightweight behavior recognition framework (GLBRF). The proposed system learns by utilizing 2D CNN for a single frame, which enables efficient behavior recognition by minimizing the amount of computation. In addition, by applying location-based grouping to reflect the interaction information between people, we focus on improving the accuracy of behavior recognition with less computational cost. This approach can operate efficiently on devices with limited resources and can reduce computation while maintaining the accuracy of behavior recognition. In other words, the proposed system simultaneously achieves the two main goals of minimizing the amount of computation required for real-time behavior recognition and improving accuracy. The main contributions of our work are as follows:  
1. A lightweight framework: GLBRF is based on 2D CNN, which can accurately recognize human behavior in a video while significantly reducing computational cost. This means that it can be effectively used in resource-constrained environments and presents a new approach in the field of video-based behavior recognition that reduces computational overhead while providing effective recognition results. 2. Improved recognition accuracy through grouping: We improved the accuracy of behavior recognition by applying grouping techniques to account for interactions between objects. This is especially useful for recognizing complex behavior patterns where multiple objects interact. 3.